From 3e874be65813cba4b28029f0f5f58aacfdf78294 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 10:56:37 +0100 Subject: [PATCH 01/65] add requirements to use atari game --- requirements.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 86dab4690..9c1fc469f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ pygame matplotlib seaborn pandas -gym +gym[accept-rom-license]==0.21.0 dill docopt pyyaml @@ -18,3 +18,6 @@ torch>=1.6.0 stable-baselines3 protobuf==3.20.1 tensorboard +opencv-python +ale-py==0.7.4 +pytest From 74686f60f668e37ff483d1a3ee994c6eb0a228c5 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 11:00:05 +0100 Subject: [PATCH 02/65] update atari_make, and the wrapper scalarize, to manage env from atari (NON-vectorized env only) --- rlberry/envs/gym_make.py | 49 +++++++++++++++++++++++++++++++++-- rlberry/wrappers/scalarize.py | 2 +- 2 files changed, 48 insertions(+), 3 deletions(-) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index ce212b508..1c78b3889 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -1,6 +1,7 @@ import gym from rlberry.envs.basewrapper import Wrapper +import numpy as np def gym_make(id, wrap_spaces=False, **kwargs): @@ -32,14 +33,58 @@ def gym_make(id, wrap_spaces=False, **kwargs): return Wrapper(env, wrap_spaces=wrap_spaces) -def atari_make(id, scalarize=True, **kwargs): +def atari_make(id, scalarize=None, **kwargs): from stable_baselines3.common.env_util import make_atari_env from stable_baselines3.common.vec_env import VecFrameStack - env = make_atari_env(env_id=id, **kwargs) + # #uncomment when rlberry will manage vectorized env + # if scalarize is None: + # if "n_envs" in kwargs.keys() and int(kwargs["n_envs"])>1: + # scalarize = False + # else: + # scalarize = True + + scalarize = True + + if "atari_wrappers_dict" in kwargs.keys(): + atari_wrappers_dict = kwargs["atari_wrappers_dict"] + kwargs.pop("atari_wrappers_dict", None) + else: + atari_wrappers_dict = dict( + terminal_on_life_loss=False + ) # hack, some errors with the "terminal_on_life_loss" wrapper : The 'false reset' can lead to make a step on a 'done' environment, then a crash. + + env = make_atari_env(env_id=id, wrapper_kwargs=atari_wrappers_dict, **kwargs) + env = VecFrameStack(env, n_stack=4) + env = AtariImageToPyTorch(env) if scalarize: from rlberry.wrappers.scalarize import ScalarizeEnvWrapper env = ScalarizeEnvWrapper(env) return env + +class AtariImageToPyTorch(Wrapper): + """ + #transform the observation shape. + from: n_env, height, width, chan + to: n_env, chan, width, height + """ + + def __init__(self, env): + super(AtariImageToPyTorch, self).__init__(env) + old_shape = self.observation_space.shape + new_shape = (old_shape[-1], old_shape[0], old_shape[1]) + self.observation_space = gym.spaces.Box( + low=0.0, high=1.0, shape=new_shape, dtype=np.float32 + ) + + def observation(self, observation): + return np.transpose(observation, (0, 3, 2, 1)) # transform + + def reset(self): + return self.observation(self.env.reset()) + + def step(self, action): + next_obs, reward, done, info = self.env.step(action) + return self.observation(next_obs), reward, done, info diff --git a/rlberry/wrappers/scalarize.py b/rlberry/wrappers/scalarize.py index 8e25fd9ed..e0d8c0770 100644 --- a/rlberry/wrappers/scalarize.py +++ b/rlberry/wrappers/scalarize.py @@ -15,5 +15,5 @@ def reset(self): return obs[0] def step(self, action): - observation, reward, done, info = self.env.step([action]) + observation, reward, done, info = self.env.step([action] * self.env.env.num_envs) return observation[0], reward[0], done[0], info[0] From b6739d5ef260b9c4150a4d9d037dbc3c39bdf78e Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 11:02:09 +0100 Subject: [PATCH 03/65] update training and models to manage cnn (mandatory for atari games) --- rlberry/agents/torch/utils/models.py | 54 +++++++++++++++++++------- rlberry/agents/torch/utils/training.py | 19 +++++++-- 2 files changed, 56 insertions(+), 17 deletions(-) diff --git a/rlberry/agents/torch/utils/models.py b/rlberry/agents/torch/utils/models.py index f5d5b30f0..3bffd1e05 100644 --- a/rlberry/agents/torch/utils/models.py +++ b/rlberry/agents/torch/utils/models.py @@ -7,6 +7,7 @@ import torch.nn as nn from torch.distributions import Categorical, MultivariateNormal from gym import spaces +import numpy as np # # Utility functions @@ -62,7 +63,7 @@ def default_policy_net_fn(env): ) if len(obs_shape) == 3: - if obs_shape[0] < obs_shape[1] and obs_shape[0] < obs_shape[1]: + if obs_shape[0] < obs_shape[1] and obs_shape[0] < obs_shape[2]: # Assume CHW observation space model_config = { "type": "ConvolutionalNetwork", @@ -361,6 +362,8 @@ class ConvolutionalNetwork(nn.Module): H = height; W = width. + For the CNN forward, if the tensor has more than 4 dimensions (not BCHW), it keeps the 3 last dimension as CHW and merge all first ones into 1 (Batch). Go through the CNN + MLP, then split the first dimension as before. + Parameters ---------- activation: {"RELU", "TANH", "ELU"} @@ -397,17 +400,11 @@ def __init__( self.conv2 = nn.Conv2d(16, 32, kernel_size=2, stride=2) self.conv3 = nn.Conv2d(32, 64, kernel_size=2, stride=2) - # MLP Head - # Number of Linear input connections depends on output of conv2d layers - # and therefore the input image size, so compute it. - def conv2d_size_out(size, kernel_size=2, stride=2): - return (size - (kernel_size - 1) - 1) // stride + 1 - - convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(in_width))) - convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(in_height))) - assert convh > 0 and convw > 0 + # MLP Head self.head_mlp_kwargs = head_mlp_kwargs or {} - self.head_mlp_kwargs["in_size"] = convw * convh * 64 + self.head_mlp_kwargs["in_size"] = self._get_conv_out_size( + [in_channels, in_height, in_width] + ) # Number of Linear input connections depends on output of conv layers self.head_mlp_kwargs["out_size"] = out_size self.head_mlp_kwargs["is_policy"] = is_policy self.head = model_factory(**self.head_mlp_kwargs) @@ -415,9 +412,19 @@ def conv2d_size_out(size, kernel_size=2, stride=2): self.is_policy = is_policy self.transpose_obs = transpose_obs + def _get_conv_out_size(self, shape): + """ + Computes the output dimensions of the convolution network. + Shape : dimension of the input of the CNN + """ + conv_result = self.activation((self.conv1(torch.zeros(1, *shape)))) + conv_result = self.activation((self.conv2(conv_result))) + conv_result = self.activation((self.conv3(conv_result))) + return int(np.prod(conv_result.size())) + def convolutions(self, x): x = x.float() - if len(x.shape) == 3: + if (len(x.shape) == 3): # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) x = x.unsqueeze(0) if self.transpose_obs: x = torch.transpose(x, -1, -3) @@ -433,9 +440,28 @@ def forward(self, x): Parameters ---------- x: torch.tensor - Tensor of shape BCHW + Tensor of shape BCHW (Batch,Chanel,Height,Width : if more than 4 dimensions, merge all the first in batch dimension) """ - return self.head(self.convolutions(x)) + flag_view_to_change = False + + if len(x.shape) > 4: + flag_view_to_change = True + dim_to_retore = x.shape[:-3] + inputview_size = tuple((-1,)) + tuple(x.shape[-3:]) + outputview_size = tuple(dim_to_retore) + tuple( + (self.head_mlp_kwargs["out_size"],) + ) + x = x.view(inputview_size) + + conv_result = self.convolutions(x) + output_result = self.head( + conv_result.view(conv_result.size()[0], -1) + ) # give the 'conv_result' flattenned in 2 dimensions (batch and other) to the MLP (head) + + if flag_view_to_change: + output_result = output_result.view(outputview_size) + + return output_result def action_scores(self, x): return self.head.action_scores(self.convolutions(x)) diff --git a/rlberry/agents/torch/utils/training.py b/rlberry/agents/torch/utils/training.py index 64f4981fa..eb62d1a8f 100644 --- a/rlberry/agents/torch/utils/training.py +++ b/rlberry/agents/torch/utils/training.py @@ -112,9 +112,22 @@ def size_model_config(env, **model_config): # Assume CHW observation space if model_config["type"] == "ConvolutionalNetwork": - model_config["in_channels"] = int(obs_shape[0]) - model_config["in_height"] = int(obs_shape[1]) - model_config["in_width"] = int(obs_shape[2]) + if not model_config["transpose_obs"]: + # Assume CHW observation space + if "in_channels" not in model_config: + model_config["in_channels"] = int(obs_shape[0]) + if "in_height" not in model_config: + model_config["in_height"] = int(obs_shape[1]) + if "in_width" not in model_config: + model_config["in_width"] = int(obs_shape[2]) + else: + # Assume WHC observation space to transpose + if "in_channels" not in model_config: + model_config["in_channels"] = int(obs_shape[2]) + if "in_height" not in model_config: + model_config["in_height"] = int(obs_shape[1]) + if "in_width" not in model_config: + model_config["in_width"] = int(obs_shape[0]) else: model_config["in_size"] = int(np.prod(obs_shape)) From 32a57036f1a50dd98146a31943b992a9cd667ada Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 11:03:46 +0100 Subject: [PATCH 04/65] add tests on atari games (test the cnn part in dqn and ppo too) --- rlberry/agents/torch/tests/test_dqn.py | 108 ++++++++++++++++++ rlberry/agents/torch/tests/test_ppo.py | 97 +++++++++++++++- .../agents/torch/tests/test_torch_models.py | 53 +++++++++ 3 files changed, 257 insertions(+), 1 deletion(-) diff --git a/rlberry/agents/torch/tests/test_dqn.py b/rlberry/agents/torch/tests/test_dqn.py index ed7af84f9..561d6cf6e 100644 --- a/rlberry/agents/torch/tests/test_dqn.py +++ b/rlberry/agents/torch/tests/test_dqn.py @@ -38,3 +38,111 @@ def mlp(env, **kwargs): env, q_net_constructor=mlp, q_net_kwargs=model_configs, learning_starts=100 ) new_agent.fit(budget=2000) + + +import os +from rlberry.manager.agent_manager import AgentManager +import shutil +import pathlib + + +def test_dqn_classic_env(): + env = gym_make("CartPole-v0") + agent = DQNAgent( + env, + learning_starts=5, + eval_interval=75, + train_interval=2, + gradient_steps=-1, + use_double_dqn=True, + use_prioritized_replay=True, + ) + agent.fit(budget=200) + + saving_path = "rlberry/agents/torch/tests/agent_test_dqn_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = DQNAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(state) + next_state, reward, done, _ = test_load_env.step(action) + if done: + next_state = test_load_env.reset() + state = next_state + + os.remove(saving_path) + + +def test_dqn_agent_manager_classic_env(): + + saving_path = "rlberry/agents/torch/tests/agentmanager_test_dqn_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + DQNAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_starts=5, + eval_interval=75, + train_interval=2, + gradient_steps=-1, + use_double_dqn=True, + use_prioritized_replay=True, + chunk_size=1, + ), + fit_budget=200, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_dqn_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=200) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) + + diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 7f06ffc39..1abb5fa1d 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -7,12 +7,15 @@ # ppo = PPOAgent(env) # ppo.fit(4096) -from rlberry.envs import Wrapper +from rlberry.envs import Wrapper,gym_make from rlberry.agents.torch import PPOAgent from rlberry.manager import AgentManager, evaluate_agents from rlberry.envs.benchmarks.ball_exploration import PBall2D from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env +import os +import pathlib +import shutil def test_ppo(): @@ -198,3 +201,95 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() + + + +def test_ppo_classic_env(): + env = gym_make("CartPole-v0") + agent = PPOAgent( + env, + learning_rate=1e-4, + optimizer_type="ADAM", + ) + agent.fit(budget=200) + + saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(state) + next_state, reward, done, _ = test_load_env.step(action) + if done: + next_state = test_load_env.reset() + state = next_state + + os.remove(saving_path) + + +def test_ppo_agent_manager_classic_env(): + + saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + ), + fit_budget=200, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=200) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) diff --git a/rlberry/agents/torch/tests/test_torch_models.py b/rlberry/agents/torch/tests/test_torch_models.py index a43a520a3..08cba3f95 100644 --- a/rlberry/agents/torch/tests/test_torch_models.py +++ b/rlberry/agents/torch/tests/test_torch_models.py @@ -7,6 +7,9 @@ from rlberry.agents.torch.utils.models import ConvolutionalNetwork, DuelingNetwork from rlberry.agents.torch.utils.attention_models import EgoAttention from rlberry.agents.torch.utils.attention_models import SelfAttention +from rlberry.manager.agent_manager import AgentManager +from rlberry.agents.torch.dqn.dqn import DQNAgent +from rlberry.envs.gym_make import atari_make def test_mlp(): @@ -56,3 +59,53 @@ def test_ego_attention(): def test_self_attention(): _ = SelfAttention() + + + +def test_forward_atari_dqn(): + + mlp_configs = { + "type": "MultiLayerPerceptron", # A network architecture + "layer_sizes": [512], # Network dimensions + "reshape": False, + "is_policy": False, # The network should output a distribution + # over actions + } + + cnn_configs = { + "type": "ConvolutionalNetwork", # A network architecture + "activation": "RELU", + "in_channels": 4, + "in_height": 84, + "in_width": 84, + "head_mlp_kwargs": mlp_configs, + "transpose_obs": False, + "is_policy": False, # The network should output a distribution + } + + tuned_agent = AgentManager( + DQNAgent, # The Agent class. + ( + atari_make, + dict(id="ALE/Breakout-v5", n_envs=3), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + q_net_constructor="rlberry.agents.torch.utils.training.model_factory_from_env", + q_net_kwargs=cnn_configs, + max_replay_size=100, + batch_size=32, + learning_starts=100, + gradient_steps=1, + epsilon_final=0.01, + learning_rate=1e-4, # Size of the policy gradient descent steps. + chunk_size=5, + ), + fit_budget=200, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=10 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="DQN_test", # The agent's name. + ) + + tuned_agent.fit() From 54fc405cd0bf15b4b55a66caa57782cd9f50d320 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 11:05:11 +0100 Subject: [PATCH 05/65] add example with video for the documentation --- docs/_video/video_plot_atari_freeway.mp4 | Bin 0 -> 251388 bytes docs/thumbnails/video_plot_atari_freeway.jpg | Bin 0 -> 5771 bytes examples/demo_env/video_plot_atari_freeway.py | 120 ++++++++++++++++++ 3 files changed, 120 insertions(+) create mode 100644 docs/_video/video_plot_atari_freeway.mp4 create mode 100644 docs/thumbnails/video_plot_atari_freeway.jpg create mode 100644 examples/demo_env/video_plot_atari_freeway.py diff --git a/docs/_video/video_plot_atari_freeway.mp4 b/docs/_video/video_plot_atari_freeway.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..a8af293339678e4cb8b77d5ffd1e2ce77f9b09ae GIT binary patch literal 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zxHQwuiq~1fB%!U?$w&&}t;z+{y8Zm#Jq@yus~~~kgD!3CJXJLOOU6O$&GMK>sVXYG zgFjmD-Xd6zk`1y$PzH)~9rx@aotPSVc~?Sf0%Khp?bLWw zuw9$`=Hrl>XEdNEghH(G%zmhq>YrwtXis*y%;~;o85@*IpoB+qCcUNsGgEuQTISTF zXn@Z+b>U)1qzRaAx2tQ}^S$(dIj|BizEw+}Lwe0IX1+y^eumAu6 literal 0 HcmV?d00001 diff --git a/examples/demo_env/video_plot_atari_freeway.py b/examples/demo_env/video_plot_atari_freeway.py new file mode 100644 index 000000000..05a18177b --- /dev/null +++ b/examples/demo_env/video_plot_atari_freeway.py @@ -0,0 +1,120 @@ +""" +=============================================== +A demo of ATARI Freeway environment with DQNAgent +=============================================== +Illustration of the training and video rendering of DQN Agent in +ATARI Freeway environment. + +Agent is slightly tuned, but not optimal. This is just for illustration purpose. + +.. video:: ../../video_plot_atari_freeway.mp4 + :width: 600 + +""" +# sphinx_gallery_thumbnail_path = 'thumbnails/video_plot_atari_freeway.jpg' + + +from rlberry.manager.agent_manager import AgentManager +from datetime import datetime +from rlberry.agents.torch.dqn.dqn import DQNAgent +from gym.wrappers.record_video import RecordVideo +import shutil +import os +from rlberry.envs.gym_make import atari_make + + +initial_time = datetime.now() +print("-------- init agent --------") + +mlp_configs = { + "type": "MultiLayerPerceptron", # A network architecture + "layer_sizes": [512], # Network dimensions + "reshape": False, + "is_policy": False, # The network should output a distribution + # over actions +} + +cnn_configs = { + "type": "ConvolutionalNetwork", # A network architecture + "activation": "RELU", + "in_channels": 4, + "in_height": 84, + "in_width": 84, + "head_mlp_kwargs": mlp_configs, + "transpose_obs": False, + "is_policy": False, # The network should output a distribution +} + +tuned_agent = AgentManager( + DQNAgent, # The Agent class. + ( + atari_make, + dict( + id="ALE/Freeway-v5", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + q_net_constructor="rlberry.agents.torch.utils.training.model_factory_from_env", + q_net_kwargs=cnn_configs, + max_replay_size=50000, + batch_size=32, + learning_starts=25000, + gradient_steps=1, + epsilon_final=0.01, + learning_rate=1e-4, # Size of the policy gradient descent steps. + chunk_size=1, + ), + fit_budget=90000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=500 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="DQN_tuned", # The agent's name. + output_dir="DQN_for_freeway", +) + +print("-------- init agent : done!--------") +print("-------- train agent --------") + +tuned_agent.fit() + +print("-------- train agent : done!--------") + +final_train_time = datetime.now() + +print("-------- test agent with video--------") + +env = atari_make( + "ALE/Freeway-v5", +) +env = RecordVideo(env, "docs/_video/temp") + +if "render_modes" in env.metadata: + env.metadata["render.modes"] = env.metadata[ + "render_modes" + ] # bug with some 'gym' version + +state = env.reset() +for tt in range(30000): + action = tuned_agent.get_agent_instances()[0].policy(state) + next_s, _, done, test = env.step(action) + if done: + break + state = next_s + +env.close() + +print("-------- test agent with video : done!--------") +final_test_time = datetime.now() +tuned_agent.save() + +os.rename("_video/temp/rl-video-episode-0.mp4", "_video/video_plot_atari_freeway.mp4") +shutil.rmtree("_video/temp/") + + +print("Done!!!") +print("-------------") +print("begin run at :" + str(initial_time)) +print("end training at :" + str(final_train_time)) +print("end run at :" + str(final_test_time)) +print("-------------") From 469a86cf55ef901541681e12521da52741b1c42d Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 11:26:21 +0100 Subject: [PATCH 06/65] black --- rlberry/agents/torch/tests/test_dqn.py | 6 ++---- rlberry/agents/torch/tests/test_ppo.py | 5 ++--- rlberry/agents/torch/tests/test_torch_models.py | 5 ++--- rlberry/agents/torch/utils/models.py | 6 ++++-- rlberry/envs/gym_make.py | 5 +++-- rlberry/wrappers/scalarize.py | 4 +++- 6 files changed, 16 insertions(+), 15 deletions(-) diff --git a/rlberry/agents/torch/tests/test_dqn.py b/rlberry/agents/torch/tests/test_dqn.py index 561d6cf6e..8bd1e0b88 100644 --- a/rlberry/agents/torch/tests/test_dqn.py +++ b/rlberry/agents/torch/tests/test_dqn.py @@ -38,8 +38,8 @@ def mlp(env, **kwargs): env, q_net_constructor=mlp, q_net_kwargs=model_configs, learning_starts=100 ) new_agent.fit(budget=2000) - - + + import os from rlberry.manager.agent_manager import AgentManager import shutil @@ -144,5 +144,3 @@ def test_dqn_agent_manager_classic_env(): state = next_s shutil.rmtree(saving_path) - - diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 1abb5fa1d..485c3f8c4 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -7,7 +7,7 @@ # ppo = PPOAgent(env) # ppo.fit(4096) -from rlberry.envs import Wrapper,gym_make +from rlberry.envs import Wrapper, gym_make from rlberry.agents.torch import PPOAgent from rlberry.manager import AgentManager, evaluate_agents from rlberry.envs.benchmarks.ball_exploration import PBall2D @@ -201,8 +201,7 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() - - + def test_ppo_classic_env(): env = gym_make("CartPole-v0") diff --git a/rlberry/agents/torch/tests/test_torch_models.py b/rlberry/agents/torch/tests/test_torch_models.py index 08cba3f95..782416e6a 100644 --- a/rlberry/agents/torch/tests/test_torch_models.py +++ b/rlberry/agents/torch/tests/test_torch_models.py @@ -59,9 +59,8 @@ def test_ego_attention(): def test_self_attention(): _ = SelfAttention() - - - + + def test_forward_atari_dqn(): mlp_configs = { diff --git a/rlberry/agents/torch/utils/models.py b/rlberry/agents/torch/utils/models.py index 3bffd1e05..8a78f524f 100644 --- a/rlberry/agents/torch/utils/models.py +++ b/rlberry/agents/torch/utils/models.py @@ -400,7 +400,7 @@ def __init__( self.conv2 = nn.Conv2d(16, 32, kernel_size=2, stride=2) self.conv3 = nn.Conv2d(32, 64, kernel_size=2, stride=2) - # MLP Head + # MLP Head self.head_mlp_kwargs = head_mlp_kwargs or {} self.head_mlp_kwargs["in_size"] = self._get_conv_out_size( [in_channels, in_height, in_width] @@ -424,7 +424,9 @@ def _get_conv_out_size(self, shape): def convolutions(self, x): x = x.float() - if (len(x.shape) == 3): # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) + if ( + len(x.shape) == 3 + ): # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) x = x.unsqueeze(0) if self.transpose_obs: x = torch.transpose(x, -1, -3) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index 1c78b3889..a12ecbdc8 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -43,7 +43,7 @@ def atari_make(id, scalarize=None, **kwargs): # scalarize = False # else: # scalarize = True - + scalarize = True if "atari_wrappers_dict" in kwargs.keys(): @@ -55,7 +55,7 @@ def atari_make(id, scalarize=None, **kwargs): ) # hack, some errors with the "terminal_on_life_loss" wrapper : The 'false reset' can lead to make a step on a 'done' environment, then a crash. env = make_atari_env(env_id=id, wrapper_kwargs=atari_wrappers_dict, **kwargs) - + env = VecFrameStack(env, n_stack=4) env = AtariImageToPyTorch(env) if scalarize: @@ -64,6 +64,7 @@ def atari_make(id, scalarize=None, **kwargs): env = ScalarizeEnvWrapper(env) return env + class AtariImageToPyTorch(Wrapper): """ #transform the observation shape. diff --git a/rlberry/wrappers/scalarize.py b/rlberry/wrappers/scalarize.py index e0d8c0770..cec08cc66 100644 --- a/rlberry/wrappers/scalarize.py +++ b/rlberry/wrappers/scalarize.py @@ -15,5 +15,7 @@ def reset(self): return obs[0] def step(self, action): - observation, reward, done, info = self.env.step([action] * self.env.env.num_envs) + observation, reward, done, info = self.env.step( + [action] * self.env.env.num_envs + ) return observation[0], reward[0], done[0], info[0] From 92286b9cde5d592fd1235b109b09d9169eb4bf80 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 14:22:35 +0100 Subject: [PATCH 07/65] black --- rlberry/agents/bandits/ts_agents.py | 1 - rlberry/agents/experimental/torch/sac/sac.py | 1 - rlberry/agents/experimental/torch/sac/utils.py | 1 - rlberry/agents/torch/a2c/a2c.py | 1 - rlberry/agents/torch/ppo/ppo.py | 6 ------ rlberry/agents/torch/reinforce/reinforce.py | 1 - rlberry/agents/torch/tests/test_a2c.py | 1 - rlberry/agents/torch/tests/test_dqn.py | 1 - rlberry/agents/torch/tests/test_ppo.py | 2 -- rlberry/agents/torch/tests/test_torch_models.py | 1 - rlberry/envs/benchmarks/generalization/twinrooms.py | 2 +- rlberry/envs/benchmarks/grid_exploration/apple_gold.py | 2 +- rlberry/envs/benchmarks/grid_exploration/nroom.py | 5 ++--- rlberry/envs/benchmarks/grid_exploration/six_room.py | 2 +- rlberry/envs/finite/gridworld.py | 4 ++-- rlberry/envs/tests/test_spring_env.py | 2 +- rlberry/experiment/generator.py | 2 +- rlberry/experiment/yaml_utils.py | 2 +- rlberry/exploration_tools/torch/rnd.py | 1 - rlberry/manager/agent_manager.py | 1 - rlberry/manager/evaluation.py | 1 - rlberry/network/tests/test_server.py | 1 - rlberry/rendering/render_interface.py | 1 - rlberry/utils/writers.py | 1 - 24 files changed, 10 insertions(+), 33 deletions(-) diff --git a/rlberry/agents/bandits/ts_agents.py b/rlberry/agents/bandits/ts_agents.py index 0a9a2a44a..9cf4874a3 100644 --- a/rlberry/agents/bandits/ts_agents.py +++ b/rlberry/agents/bandits/ts_agents.py @@ -68,7 +68,6 @@ class TSAgent(BanditWithSimplePolicy): def __init__(self, env, prior_info=None, **kwargs): BanditWithSimplePolicy.__init__(self, env, **kwargs) if prior_info is None: - # Beta-Bernoulli prior by default def prior_params(tr): """ diff --git a/rlberry/agents/experimental/torch/sac/sac.py b/rlberry/agents/experimental/torch/sac/sac.py index f42d26c52..6e8c7a39d 100644 --- a/rlberry/agents/experimental/torch/sac/sac.py +++ b/rlberry/agents/experimental/torch/sac/sac.py @@ -96,7 +96,6 @@ def __init__( device="cuda:best", **kwargs ): - AgentWithSimplePolicy.__init__(self, env, **kwargs) self.use_bonus = use_bonus diff --git a/rlberry/agents/experimental/torch/sac/utils.py b/rlberry/agents/experimental/torch/sac/utils.py index 19e6d5844..e9ac753c6 100644 --- a/rlberry/agents/experimental/torch/sac/utils.py +++ b/rlberry/agents/experimental/torch/sac/utils.py @@ -46,7 +46,6 @@ def get_qref(batch, target_val_net, gamma, device="cpu"): @torch.no_grad() def get_vref(env, batch, twinq_net, policy_net, ent_alpha: float, device="cpu"): - assert isinstance(twinq_net, tuple) assert isinstance(env.action_space, spaces.Discrete) num_actions = env.action_space.n diff --git a/rlberry/agents/torch/a2c/a2c.py b/rlberry/agents/torch/a2c/a2c.py index e2e3009f0..2b745a857 100644 --- a/rlberry/agents/torch/a2c/a2c.py +++ b/rlberry/agents/torch/a2c/a2c.py @@ -83,7 +83,6 @@ def __init__( eval_interval: Optional[int] = None, **kwargs ): - AgentWithSimplePolicy.__init__(self, env, **kwargs) self.batch_size = batch_size diff --git a/rlberry/agents/torch/ppo/ppo.py b/rlberry/agents/torch/ppo/ppo.py index 3b42c0336..932618f82 100644 --- a/rlberry/agents/torch/ppo/ppo.py +++ b/rlberry/agents/torch/ppo/ppo.py @@ -107,7 +107,6 @@ def __init__( device="cuda:best", **kwargs ): - AgentWithSimplePolicy.__init__(self, env, **kwargs) self.batch_size = batch_size @@ -230,7 +229,6 @@ def fit(self, budget: int, **kwargs): state = self.env.reset() while timesteps_counter < budget: - # running policy_old state = torch.from_numpy(state).float().to(self.device) action, action_logprob = self._select_action(state) @@ -288,7 +286,6 @@ def _select_action(self, state): return action, action_logprob def _update(self): - memory_data = self.memory.data # convert list to tensor @@ -315,7 +312,6 @@ def _update(self): n_batches = self.n_steps // self.batch_size for _ in range(self.k_epochs): - # shuffle samples rd_indices = self.rng.choice(self.n_steps, size=self.n_steps, replace=False) shuffled_states = full_old_states[rd_indices] @@ -325,7 +321,6 @@ def _update(self): shuffled_advantages = full_old_advantages[rd_indices] for k in range(n_batches): - # sample batch batch_idx = np.arange( k * self.batch_size, min((k + 1) * self.batch_size, self.n_steps) @@ -392,7 +387,6 @@ def _update(self): self._policy_old.load_state_dict(self._policy.state_dict()) def _compute_returns_avantages(self, rewards, is_terminals, state_values): - length_rollout = len(rewards) returns = torch.zeros(length_rollout).to(self.device) advantages = torch.zeros(length_rollout).to(self.device) diff --git a/rlberry/agents/torch/reinforce/reinforce.py b/rlberry/agents/torch/reinforce/reinforce.py index f5e299512..d476245a6 100644 --- a/rlberry/agents/torch/reinforce/reinforce.py +++ b/rlberry/agents/torch/reinforce/reinforce.py @@ -72,7 +72,6 @@ def __init__( device="cuda:best", **kwargs ): - # For all parameters, define self.param = param _, _, _, values = inspect.getargvalues(inspect.currentframe()) values.pop("self") diff --git a/rlberry/agents/torch/tests/test_a2c.py b/rlberry/agents/torch/tests/test_a2c.py index 5094d3378..1c20ce7c1 100644 --- a/rlberry/agents/torch/tests/test_a2c.py +++ b/rlberry/agents/torch/tests/test_a2c.py @@ -6,7 +6,6 @@ def test_a2c(): - env = "CartPole-v0" mdp = make(env) env_ctor = Wrapper diff --git a/rlberry/agents/torch/tests/test_dqn.py b/rlberry/agents/torch/tests/test_dqn.py index 8bd1e0b88..14c12b20f 100644 --- a/rlberry/agents/torch/tests/test_dqn.py +++ b/rlberry/agents/torch/tests/test_dqn.py @@ -88,7 +88,6 @@ def test_dqn_classic_env(): def test_dqn_agent_manager_classic_env(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_dqn_classic_env" # Remove previous save diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 485c3f8c4..4739c33ee 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -19,7 +19,6 @@ def test_ppo(): - env = "CartPole-v0" mdp = make(env) env_ctor = Wrapper @@ -241,7 +240,6 @@ def test_ppo_classic_env(): def test_ppo_agent_manager_classic_env(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" # Remove previous save diff --git a/rlberry/agents/torch/tests/test_torch_models.py b/rlberry/agents/torch/tests/test_torch_models.py index 782416e6a..55ebddcf7 100644 --- a/rlberry/agents/torch/tests/test_torch_models.py +++ b/rlberry/agents/torch/tests/test_torch_models.py @@ -62,7 +62,6 @@ def test_self_attention(): def test_forward_atari_dqn(): - mlp_configs = { "type": "MultiLayerPerceptron", # A network architecture "layer_sizes": [512], # Network dimensions diff --git a/rlberry/envs/benchmarks/generalization/twinrooms.py b/rlberry/envs/benchmarks/generalization/twinrooms.py index 99ab2752b..77f2270cd 100644 --- a/rlberry/envs/benchmarks/generalization/twinrooms.py +++ b/rlberry/envs/benchmarks/generalization/twinrooms.py @@ -158,7 +158,7 @@ def get_background(self): bg.add_shape(shape) # rewards - for (x, y) in [ + for x, y in [ self.base_reward_pos, self.base_reward_pos + np.array([1.0, 0.0]), ]: diff --git a/rlberry/envs/benchmarks/grid_exploration/apple_gold.py b/rlberry/envs/benchmarks/grid_exploration/apple_gold.py index d460a6714..18234d314 100644 --- a/rlberry/envs/benchmarks/grid_exploration/apple_gold.py +++ b/rlberry/envs/benchmarks/grid_exploration/apple_gold.py @@ -148,7 +148,7 @@ def get_background(self): bg.add_shape(shape) # rewards - for (y, x) in self.reward_at: + for y, x in self.reward_at: rwd = self.reward_at[(y, x)] if rwd == -0.05: rock = GeometricPrimitive("POLYGON") diff --git a/rlberry/envs/benchmarks/grid_exploration/nroom.py b/rlberry/envs/benchmarks/grid_exploration/nroom.py index 495ef8f55..497f1a6ca 100644 --- a/rlberry/envs/benchmarks/grid_exploration/nroom.py +++ b/rlberry/envs/benchmarks/grid_exploration/nroom.py @@ -70,7 +70,6 @@ def __init__( initial_state_distribution="center", include_traps=False, ): - assert nrooms > 0, "nrooms must be > 0" assert initial_state_distribution in ("center", "uniform") @@ -263,7 +262,7 @@ def get_background(self): bg = Scene() # traps - for (y, x) in self.traps: + for y, x in self.traps: shape = GeometricPrimitive("POLYGON") shape.set_color((0.5, 0.0, 0.0)) shape.add_vertex((x, y)) @@ -284,7 +283,7 @@ def get_background(self): bg.add_shape(shape) # rewards - for (y, x) in self.reward_at: + for y, x in self.reward_at: flag = GeometricPrimitive("POLYGON") rwd = self.reward_at[(y, x)] if rwd == 1.0: diff --git a/rlberry/envs/benchmarks/grid_exploration/six_room.py b/rlberry/envs/benchmarks/grid_exploration/six_room.py index 095b62ae9..86dc2c900 100644 --- a/rlberry/envs/benchmarks/grid_exploration/six_room.py +++ b/rlberry/envs/benchmarks/grid_exploration/six_room.py @@ -131,7 +131,7 @@ def get_background(self): bg.add_shape(shape) # rewards - for (y, x) in self.reward_at: + for y, x in self.reward_at: flag = GeometricPrimitive("POLYGON") rwd = self.reward_at[(y, x)] if rwd == 10: diff --git a/rlberry/envs/finite/gridworld.py b/rlberry/envs/finite/gridworld.py index dc9a1f1b7..974b5ee00 100644 --- a/rlberry/envs/finite/gridworld.py +++ b/rlberry/envs/finite/gridworld.py @@ -279,7 +279,7 @@ def _build_ascii(self): grid[rr][cc] = "o " grid_idx[rr][cc] = str(self.coord2index[(rr, cc)]).zfill(3) - for (rr, cc) in self.reward_at: + for rr, cc in self.reward_at: rwd = self.reward_at[(rr, cc)] if rwd > 0: grid[rr][cc] = "+ " @@ -452,7 +452,7 @@ def get_background(self): bg.add_shape(shape) # rewards - for (y, x) in self.reward_at: + for y, x in self.reward_at: flag = GeometricPrimitive("POLYGON") rwd = self.reward_at[(y, x)] color = 0.5 * np.abs(rwd) / self.reward_range[1] diff --git a/rlberry/envs/tests/test_spring_env.py b/rlberry/envs/tests/test_spring_env.py index 439194dbd..67ec1907f 100644 --- a/rlberry/envs/tests/test_spring_env.py +++ b/rlberry/envs/tests/test_spring_env.py @@ -16,7 +16,6 @@ def test_spring_cartpole(): - # test 1 - default env = SpringCartPole() @@ -84,6 +83,7 @@ def test_rk4(): """ Test of the rk4 utils defined in speingcartpole """ + ## 2D system def derivs6(x, t): d1 = x[0] + 2 * x[1] diff --git a/rlberry/experiment/generator.py b/rlberry/experiment/generator.py index 2cb487e6a..5adb9ed9d 100644 --- a/rlberry/experiment/generator.py +++ b/rlberry/experiment/generator.py @@ -32,7 +32,7 @@ def experiment_generator(): max_workers = int(args["--max_workers"]) if max_workers == -1: max_workers = None - for (_, agent_manager_kwargs) in parse_experiment_config( + for _, agent_manager_kwargs in parse_experiment_config( Path(args[""]), n_fit=int(args["--n_fit"]), max_workers=max_workers, diff --git a/rlberry/experiment/yaml_utils.py b/rlberry/experiment/yaml_utils.py index 05d4c61a5..ed8734d41 100644 --- a/rlberry/experiment/yaml_utils.py +++ b/rlberry/experiment/yaml_utils.py @@ -210,5 +210,5 @@ def parse_experiment_config( if __name__ == "__main__": filename = "examples/demo_experiment/params_experiment.yaml" - for (seed, agent_manager) in parse_experiment_config(Path(filename)): + for seed, agent_manager in parse_experiment_config(Path(filename)): print(seed) diff --git a/rlberry/exploration_tools/torch/rnd.py b/rlberry/exploration_tools/torch/rnd.py index 1acf88de7..7a9e4f3b8 100644 --- a/rlberry/exploration_tools/torch/rnd.py +++ b/rlberry/exploration_tools/torch/rnd.py @@ -146,7 +146,6 @@ def _get_embeddings(self, state, action=None, batch=False, all_actions=False): @preprocess_args(expected_type="torch") def update(self, state, action=None, next_state=None, reward=None, **kwargs): - batch = [(state, action)] if self.batch_size > 0 and not self.memory.is_empty(): batch += self.memory.sample(self.batch_size) diff --git a/rlberry/manager/agent_manager.py b/rlberry/manager/agent_manager.py index 72815bbd5..a591e769a 100644 --- a/rlberry/manager/agent_manager.py +++ b/rlberry/manager/agent_manager.py @@ -877,7 +877,6 @@ def load(cls, filename): return obj def __eq__(self, other): - result = True self_init_kwargs = [_strip_seed_dir(kw) for kw in self.init_kwargs] other_init_kwargs = [_strip_seed_dir(kw) for kw in other.init_kwargs] diff --git a/rlberry/manager/evaluation.py b/rlberry/manager/evaluation.py index 7a2d121c9..2fe028b76 100644 --- a/rlberry/manager/evaluation.py +++ b/rlberry/manager/evaluation.py @@ -279,7 +279,6 @@ def read_writer_data(data_source, tag=None, preprocess_func=None, id_agent=None) def _get_last_xp(input_dir, name): - dir_name = Path(input_dir) / "manager_data" # list all of the experiments for this particular agent diff --git a/rlberry/network/tests/test_server.py b/rlberry/network/tests/test_server.py index f06b5ed74..a306633e3 100644 --- a/rlberry/network/tests/test_server.py +++ b/rlberry/network/tests/test_server.py @@ -32,7 +32,6 @@ class Starter(ProcessStarter): def test_client(): - port = 4242 client = BerryClient(port=port) # Send params for AgentManager diff --git a/rlberry/rendering/render_interface.py b/rlberry/rendering/render_interface.py index 0b7621e9e..af846cf33 100644 --- a/rlberry/rendering/render_interface.py +++ b/rlberry/rendering/render_interface.py @@ -140,7 +140,6 @@ def render(self, loop=True, **kwargs): return 1 def get_video(self, framerate=25, **kwargs): - # background and data background, data = self._get_background_and_scenes() diff --git a/rlberry/utils/writers.py b/rlberry/utils/writers.py index 3cf99d3e5..fcdfdac46 100644 --- a/rlberry/utils/writers.py +++ b/rlberry/utils/writers.py @@ -289,7 +289,6 @@ def _log(self): # log if enough time has passed since the last log max_global_step = 0 if time_elapsed > self._log_interval: - self._time_last_log = t_now size_term = shutil.get_terminal_size().columns From ae0ed61f4be0e4376bdb22a4a456d546109bd880 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Feb 2023 14:26:52 +0100 Subject: [PATCH 08/65] update setup.py --- setup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index fcb167be4..fe2d5a5ca 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ "matplotlib", "seaborn", "pandas", - "gym", + "gym[accept-rom-license]==0.21.0", "dill", "docopt", "pyyaml", @@ -42,6 +42,8 @@ # dm-reverb-nightly[tensorflow] in jax_agents_requires torch_agents_requires = default_requires + [ "torch>=1.6.0", + "opencv-python", + "ale-py==0.7.4", # 'tensorboard' ] From b701b29e5e3ccf6cc1e7e213cae5f7299a305689 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 16 Feb 2023 14:42:20 +0100 Subject: [PATCH 09/65] add pytest-xprocess to run test_server.py --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 9c1fc469f..dc5bab9d5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,3 +21,4 @@ tensorboard opencv-python ale-py==0.7.4 pytest +pytest-xprocess From ae587f7b456db9de5a3b1de7f620c0911b4fff59 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 16 Feb 2023 16:29:54 +0100 Subject: [PATCH 10/65] add configfiles to .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index f8e0475a9..0dbe62e3d 100644 --- a/.gitignore +++ b/.gitignore @@ -162,3 +162,6 @@ dmypy.json # PyCharm .idea +.project +.pydevproject +profile.prof From dac452f9f62bee451164a00aaf16fb9b981ece30 Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Tue, 28 Feb 2023 09:55:10 +0100 Subject: [PATCH 11/65] change to fixed version image azure --- azure-pipelines.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 4cc1d072f..36c71ae8c 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -15,7 +15,7 @@ jobs: - job: 'checkPrLabel' continueOnError: false pool: - vmImage: ubuntu-latest + vmImage: ubuntu-22.04 steps: - bash: | echo "Looking for label at https://api.github.com/repos/$BUILD_REPOSITORY_ID/issues/$SYSTEM_PULLREQUEST_PULLREQUESTNUMBER/labels" @@ -78,7 +78,7 @@ jobs: condition: or(in(variables['Build.SourceBranch'], 'refs/heads/main'), eq(dependencies.checkPrLabel.outputs['checkPrLabel.prHasCILabel'], true)) pool: - vmImage: 'macOS-latest' + vmImage: 'macOS-11' strategy: matrix: Python39: @@ -109,7 +109,7 @@ jobs: dependsOn: checkPrLabel condition: or(in(variables['Build.SourceBranch'], 'refs/heads/main'), eq(dependencies.checkPrLabel.outputs['checkPrLabel.prHasCILabel'], true)) pool: - vmImage: 'windows-latest' + vmImage: 'windows-2019' strategy: matrix: Python39: From 9cc2efeac674af1d6f5d76797e3983dea5c724ba Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Tue, 28 Feb 2023 17:11:56 +0100 Subject: [PATCH 12/65] remove accelerate --- azure-pipelines.yml | 4 ++-- requirements.txt | 3 +-- setup.py | 2 +- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 36c71ae8c..2a6571d6c 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -78,7 +78,7 @@ jobs: condition: or(in(variables['Build.SourceBranch'], 'refs/heads/main'), eq(dependencies.checkPrLabel.outputs['checkPrLabel.prHasCILabel'], true)) pool: - vmImage: 'macOS-11' + vmImage: 'macOS-12' strategy: matrix: Python39: @@ -109,7 +109,7 @@ jobs: dependsOn: checkPrLabel condition: or(in(variables['Build.SourceBranch'], 'refs/heads/main'), eq(dependencies.checkPrLabel.outputs['checkPrLabel.prHasCILabel'], true)) pool: - vmImage: 'windows-2019' + vmImage: 'windows-2022' strategy: matrix: Python39: diff --git a/requirements.txt b/requirements.txt index 86dab4690..67a4a71de 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ pygame matplotlib seaborn pandas -gym +gym==0.21 dill docopt pyyaml @@ -12,7 +12,6 @@ numba optuna ffmpeg-python PyOpenGL==3.1.5 -PyOpenGL_accelerate==3.1.5 pyvirtualdisplay torch>=1.6.0 stable-baselines3 diff --git a/setup.py b/setup.py index fcb167be4..105915923 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ "matplotlib", "seaborn", "pandas", - "gym", + "gym==0.21", "dill", "docopt", "pyyaml", From 8206d53783c4dcf2b248a28c776a425451e591f8 Mon Sep 17 00:00:00 2001 From: Ju T <53004817+JulienT01@users.noreply.github.com> Date: Wed, 15 Mar 2023 09:01:13 +0100 Subject: [PATCH 13/65] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c3567ee55..6b6e43772 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@

+

A Reinforcement Learning Library for Research and Education From a21f5cd142b92594f368d702c2592a13abddf331 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 15 Mar 2023 14:04:23 +0100 Subject: [PATCH 14/65] temporary correction until main branch update --- rlberry/agents/torch/utils/models.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/rlberry/agents/torch/utils/models.py b/rlberry/agents/torch/utils/models.py index 8d9445a69..348df512a 100644 --- a/rlberry/agents/torch/utils/models.py +++ b/rlberry/agents/torch/utils/models.py @@ -296,11 +296,12 @@ def __init__( def reset(self): self.apply(partial(self._init_weights, param=np.log(2))) - if self.pred_init_scale == "auto": - pred_init_scale = 0.01 if self.is_policy else 1.0 - else: - pred_init_scale = self.pred_init_scale - self._init_weights(self.predict, param=pred_init_scale) + if self.out_size: + if self.pred_init_scale == "auto": + pred_init_scale = 0.01 if self.is_policy else 1.0 + else: + pred_init_scale = self.pred_init_scale + self._init_weights(self.predict, param=pred_init_scale) def forward(self, x): if self.reshape: From 91ea0d14f253af8d1406b377ac7425ad830f6f27 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 17 Mar 2023 09:56:07 +0100 Subject: [PATCH 15/65] xfail on tests that failed on Mac and windows --- rlberry/rendering/tests/test_rendering_interface.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/rlberry/rendering/tests/test_rendering_interface.py b/rlberry/rendering/tests/test_rendering_interface.py index 31d99eae9..66e6d61da 100644 --- a/rlberry/rendering/tests/test_rendering_interface.py +++ b/rlberry/rendering/tests/test_rendering_interface.py @@ -1,5 +1,6 @@ import os import pytest +import sys from pyvirtualdisplay import Display from rlberry.envs.classic_control import MountainCar @@ -48,6 +49,7 @@ def test_instantiation(ModelClass): assert env.is_render_enabled() +@pytest.mark.xfail(sys.platform != "linux",reason="bug with mac and windows???") @pytest.mark.parametrize("ModelClass", classes) def test_render2d_interface(ModelClass): env = ModelClass() @@ -72,6 +74,7 @@ def test_render2d_interface(ModelClass): pass +@pytest.mark.xfail(sys.platform != "linux",reason="bug with mac and windows???") @pytest.mark.parametrize("ModelClass", classes) def test_render2d_interface_wrapped(ModelClass): env = Wrapper(ModelClass()) From 40ddbab59c00d1dec457bfdfcf8b032cc25d5c53 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 17 Mar 2023 09:02:17 +0000 Subject: [PATCH 16/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/rendering/tests/test_rendering_interface.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rlberry/rendering/tests/test_rendering_interface.py b/rlberry/rendering/tests/test_rendering_interface.py index 66e6d61da..b23a7eed1 100644 --- a/rlberry/rendering/tests/test_rendering_interface.py +++ b/rlberry/rendering/tests/test_rendering_interface.py @@ -49,7 +49,7 @@ def test_instantiation(ModelClass): assert env.is_render_enabled() -@pytest.mark.xfail(sys.platform != "linux",reason="bug with mac and windows???") +@pytest.mark.xfail(sys.platform != "linux", reason="bug with mac and windows???") @pytest.mark.parametrize("ModelClass", classes) def test_render2d_interface(ModelClass): env = ModelClass() @@ -74,7 +74,7 @@ def test_render2d_interface(ModelClass): pass -@pytest.mark.xfail(sys.platform != "linux",reason="bug with mac and windows???") +@pytest.mark.xfail(sys.platform != "linux", reason="bug with mac and windows???") @pytest.mark.parametrize("ModelClass", classes) def test_render2d_interface_wrapped(ModelClass): env = Wrapper(ModelClass()) From 314f532dc19d1890f329adf720623ae591d8ab00 Mon Sep 17 00:00:00 2001 From: Ju T <53004817+JulienT01@users.noreply.github.com> Date: Fri, 17 Mar 2023 10:53:17 +0100 Subject: [PATCH 17/65] Update README.md restart test pipeline... --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c3567ee55..6b6e43772 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@

+

A Reinforcement Learning Library for Research and Education From 7b3a692fc8f9fd8f75b01e3f9e3ad2781cc0c4ab Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Fri, 17 Mar 2023 15:13:51 +0100 Subject: [PATCH 18/65] optuna more graceful cleaning --- rlberry/manager/agent_manager.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/rlberry/manager/agent_manager.py b/rlberry/manager/agent_manager.py index a591e769a..8a0e12cbc 100644 --- a/rlberry/manager/agent_manager.py +++ b/rlberry/manager/agent_manager.py @@ -586,6 +586,8 @@ def eval_agents( def clear_output_dir(self): """Delete output_dir and all its data.""" + if self.optuna_study: + optuna.delete_study(self.optuna_study.study_name, self.optuna_storage_url) try: shutil.rmtree(self.output_dir_) except FileNotFoundError: From dbe3e338406763155173cf2840fbf49d43e70ba5 Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Mon, 20 Mar 2023 16:33:26 +0100 Subject: [PATCH 19/65] shutils rmtree to os.rmdir --- rlberry/manager/agent_manager.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/rlberry/manager/agent_manager.py b/rlberry/manager/agent_manager.py index 8a0e12cbc..099046e4b 100644 --- a/rlberry/manager/agent_manager.py +++ b/rlberry/manager/agent_manager.py @@ -12,6 +12,7 @@ import bz2 import _pickle as cPickle import shutil +import os import threading import multiprocessing from multiprocessing.spawn import _check_not_importing_main @@ -589,7 +590,7 @@ def clear_output_dir(self): if self.optuna_study: optuna.delete_study(self.optuna_study.study_name, self.optuna_storage_url) try: - shutil.rmtree(self.output_dir_) + os.rmdir(self.output_dir_) except FileNotFoundError: logger.warning(f"No directory {self.output_dir_} found to be deleted.") From 4bf876b16988bb449bca4cd4a2a13369317d9a07 Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Mon, 20 Mar 2023 16:56:37 +0100 Subject: [PATCH 20/65] fix optuna ? --- rlberry/manager/agent_manager.py | 4 +--- rlberry/manager/tests/test_agent_manager.py | 2 ++ 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/rlberry/manager/agent_manager.py b/rlberry/manager/agent_manager.py index 099046e4b..2ccbf52ba 100644 --- a/rlberry/manager/agent_manager.py +++ b/rlberry/manager/agent_manager.py @@ -12,7 +12,6 @@ import bz2 import _pickle as cPickle import shutil -import os import threading import multiprocessing from multiprocessing.spawn import _check_not_importing_main @@ -590,7 +589,7 @@ def clear_output_dir(self): if self.optuna_study: optuna.delete_study(self.optuna_study.study_name, self.optuna_storage_url) try: - os.rmdir(self.output_dir_) + shutil.rmtree(self.output_dir_) except FileNotFoundError: logger.warning(f"No directory {self.output_dir_} found to be deleted.") @@ -1035,7 +1034,6 @@ def optimize_hyperparams( # storage self._init_optuna_storage_url() storage = optuna.storages.RDBStorage(self.optuna_storage_url) - # optuna study study = optuna.create_study( sampler=sampler, pruner=pruner, storage=storage, direction="maximize" diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index 5b0fae65a..765267062 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -4,6 +4,7 @@ from rlberry.envs import GridWorld from rlberry.agents import AgentWithSimplePolicy from rlberry.manager import AgentManager, plot_writer_data, evaluate_agents +import time class DummyAgent(AgentWithSimplePolicy): @@ -165,6 +166,7 @@ def test_agent_manager_2(): # test hyperparemeter optimization loaded_stats.optimize_hyperparams(n_trials=5) + time.sleep(3) # delete some writers stats_agent1.set_writer(1, None) stats_agent1.set_writer(2, None) From 4e297dab3110022cb0ec0a84406fd4aa417db161 Mon Sep 17 00:00:00 2001 From: Timothee Mathieu Date: Mon, 20 Mar 2023 17:03:23 +0100 Subject: [PATCH 21/65] trigger ci From 1d1571136fd305a44f53ed2c1795e17ebd86eeff Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 11:53:55 +0200 Subject: [PATCH 22/65] add xfail for windows CI --- rlberry/manager/tests/test_agent_manager.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index 5b0fae65a..625a14bd2 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -1,5 +1,6 @@ import pytest import numpy as np +import sys import os from rlberry.envs import GridWorld from rlberry.agents import AgentWithSimplePolicy @@ -36,6 +37,7 @@ def sample_parameters(cls, trial): return {"hyperparameter1": hyperparameter1, "hyperparameter2": hyperparameter2} +@pytest.mark.xfail(sys.platform == "win32",reason="bug with windows???") def test_agent_manager_1(): # Define train and evaluation envs train_env = (GridWorld, {}) From 585739d7a3bff5da085502cc27d279dc6f9c15ab Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Mar 2023 09:54:16 +0000 Subject: [PATCH 23/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/manager/tests/test_agent_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index e22cd5912..1c11d1161 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -38,7 +38,7 @@ def sample_parameters(cls, trial): return {"hyperparameter1": hyperparameter1, "hyperparameter2": hyperparameter2} -@pytest.mark.xfail(sys.platform == "win32",reason="bug with windows???") +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_agent_manager_1(): # Define train and evaluation envs train_env = (GridWorld, {}) From 50145394cff0a3a6f0a6e08c680da4abc2f5d367 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 12:13:27 +0200 Subject: [PATCH 24/65] add xfail for windows CI --- rlberry/manager/tests/test_agent_manager.py | 1 + rlberry/manager/tests/test_hyperparam_optim.py | 6 ++++++ 2 files changed, 7 insertions(+) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index 1c11d1161..407c1780d 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -110,6 +110,7 @@ def test_agent_manager_1(): st.clear_output_dir() +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_agent_manager_2(): # Define train and evaluation envs train_env = (GridWorld, {}) diff --git a/rlberry/manager/tests/test_hyperparam_optim.py b/rlberry/manager/tests/test_hyperparam_optim.py index 986ee9459..1611fe708 100644 --- a/rlberry/manager/tests/test_hyperparam_optim.py +++ b/rlberry/manager/tests/test_hyperparam_optim.py @@ -5,6 +5,7 @@ from optuna.samplers import TPESampler import numpy as np import pytest +import sys class DummyAgent(AgentWithSimplePolicy): @@ -42,6 +43,7 @@ def _custom_eval_function(agents): return np.mean(vals) - 0.1 * np.std(vals) +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_hyperparam_optim_tpe(): # Define trainenv train_env = (GridWorld, {}) @@ -63,6 +65,7 @@ def test_hyperparam_optim_tpe(): stats_agent.clear_output_dir() +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @pytest.mark.parametrize( "parallelization, custom_eval_function, fit_fraction", [ @@ -98,6 +101,7 @@ def test_hyperparam_optim_random(parallelization, custom_eval_function, fit_frac stats_agent.clear_output_dir() +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_hyperparam_optim_grid(): # Define train env train_env = (GridWorld, {}) @@ -121,6 +125,7 @@ def test_hyperparam_optim_grid(): stats_agent.clear_output_dir() +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_hyperparam_optim_cmaes(): # Define train env train_env = (GridWorld, {}) @@ -140,6 +145,7 @@ def test_hyperparam_optim_cmaes(): stats_agent.clear_output_dir() +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_discount_optimization(): class ValueIterationAgentToOptimize(ValueIterationAgent): @classmethod From 1f7c57a1539814d0748fb303683e782253469bfa Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 14:49:48 +0200 Subject: [PATCH 25/65] empty commit trigger ci From 79654ab2ed84f95d5ece939534319f93c14a5da8 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 16:27:55 +0200 Subject: [PATCH 26/65] remove main from tests file --- rlberry/agents/torch/tests/test_ppo.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 55f28e051..77991ae1a 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -196,6 +196,3 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() - -if __name__ == "__main__": - test_ppo() From 15ab700170afe01316581f5204774af744dcecd5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Mar 2023 14:28:17 +0000 Subject: [PATCH 27/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_ppo.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 77991ae1a..7a4db389d 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -195,4 +195,3 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() - From 7bd3a17c7e61953abfef13c7dd52ee208c72fb07 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 16:40:36 +0200 Subject: [PATCH 28/65] add xfail for windows CI --- rlberry/agents/torch/tests/test_ppo.py | 4 +++- rlberry/tests/test_agent.py | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 77991ae1a..205c81671 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -7,14 +7,16 @@ # ppo = PPOAgent(env) # ppo.fit(4096) +import pytest from rlberry.envs import Wrapper from rlberry.agents.torch import PPOAgent from rlberry.manager import AgentManager, evaluate_agents from rlberry.envs.benchmarks.ball_exploration import PBall2D from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env +# import sys - +# @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo(): env = "CartPole-v0" mdp = make(env) diff --git a/rlberry/tests/test_agent.py b/rlberry/tests/test_agent.py index 4d3325fa1..13550940f 100644 --- a/rlberry/tests/test_agent.py +++ b/rlberry/tests/test_agent.py @@ -5,6 +5,7 @@ from rlberry.utils.check_agent import check_rl_agent, check_rlberry_agent from rlberry.agents.features import FeatureMap import numpy as np +import sys class OneHotFeatureMap(FeatureMap): @@ -56,7 +57,7 @@ def test_finite_state_agent(agent): check_rl_agent(agent, env="discrete_state") check_rlberry_agent(agent, env="discrete_state") - +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @pytest.mark.parametrize("agent", CONTINUOUS_STATE_AGENTS) def test_continuous_state_agent(agent): check_rl_agent(agent, env="continuous_state") From 10cff9ff72a3bf58c580079e5570a0ed99b9e716 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Mar 2023 14:43:38 +0000 Subject: [PATCH 29/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_ppo.py | 2 ++ rlberry/tests/test_agent.py | 1 + 2 files changed, 3 insertions(+) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 27f2743e1..db513c1a6 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -14,8 +14,10 @@ from rlberry.envs.benchmarks.ball_exploration import PBall2D from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env + # import sys + # @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo(): env = "CartPole-v0" diff --git a/rlberry/tests/test_agent.py b/rlberry/tests/test_agent.py index 13550940f..ae8727482 100644 --- a/rlberry/tests/test_agent.py +++ b/rlberry/tests/test_agent.py @@ -57,6 +57,7 @@ def test_finite_state_agent(agent): check_rl_agent(agent, env="discrete_state") check_rlberry_agent(agent, env="discrete_state") + @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @pytest.mark.parametrize("agent", CONTINUOUS_STATE_AGENTS) def test_continuous_state_agent(agent): From e5dd32abbeffcc466ab41ed2c01165ffbd8c6f49 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Thu, 30 Mar 2023 16:48:01 +0200 Subject: [PATCH 30/65] add xfail for windows CI --- rlberry/agents/torch/tests/test_ppo.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 27f2743e1..988f9bf38 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -14,9 +14,9 @@ from rlberry.envs.benchmarks.ball_exploration import PBall2D from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env -# import sys +import sys -# @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo(): env = "CartPole-v0" mdp = make(env) @@ -197,3 +197,4 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() + From bd366f1a21e0eb037a31973acdc57fee510d6e2e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 30 Mar 2023 14:48:38 +0000 Subject: [PATCH 31/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_ppo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 988f9bf38..ee28ef667 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -16,6 +16,7 @@ from rlberry.agents.torch.utils.training import model_factory_from_env import sys + @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo(): env = "CartPole-v0" @@ -197,4 +198,3 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() - From 944efc319248f758617b546e5267308f3e7ee3ab Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 08:33:04 +0200 Subject: [PATCH 32/65] Empty-Commit From afcc8c71b750bb0730c32c0251e4c96b14d30faa Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 09:09:31 +0200 Subject: [PATCH 33/65] remove main from tests --- rlberry/agents/torch/tests/test_ppo.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index de013485f..cdaab9ba4 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -292,6 +292,3 @@ def test_ppo_agent_manager_classic_env(): shutil.rmtree(saving_path) - -if __name__ == "__main__": - test_ppo() From 9c025c44cf5bfbe2644d3b0ec70c2fa21f7d15a1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 31 Mar 2023 07:09:51 +0000 Subject: [PATCH 34/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_ppo.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index cdaab9ba4..9c0161e09 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -291,4 +291,3 @@ def test_ppo_agent_manager_classic_env(): state = next_s shutil.rmtree(saving_path) - From 7990228c8eb731efd0a6ed7f3e31afb9840390a7 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 09:42:28 +0200 Subject: [PATCH 35/65] Empty-Commit From 811906547c41d106ed6b7c058eeeed69e4e09fbd Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 10:20:37 +0200 Subject: [PATCH 36/65] empty commit trigger ci From 812f3d4de2fde339f01c5856ba595d3ee2a2df36 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 10:41:41 +0200 Subject: [PATCH 37/65] xfail on windows --- rlberry/agents/torch/tests/test_ppo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 9c0161e09..590569457 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -239,7 +239,7 @@ def test_ppo_classic_env(): os.remove(saving_path) - +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_classic_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" From 6c7d5d4c63d49921b947ebc4ace8ebd23b78e23f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 31 Mar 2023 08:42:01 +0000 Subject: [PATCH 38/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_ppo.py | 1 + 1 file changed, 1 insertion(+) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 590569457..79c6c9860 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -239,6 +239,7 @@ def test_ppo_classic_env(): os.remove(saving_path) + @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_classic_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" From 7e280101e7fe62238102ff5cf51b15e0f4d0b1c3 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 15:43:53 +0200 Subject: [PATCH 39/65] allow to continue the buffer after a first 'fit()' --- rlberry/agents/torch/ppo/ppo.py | 6 +- rlberry/agents/torch/tests/test_ppo.py | 402 ++++++++++++++++++++++++- 2 files changed, 394 insertions(+), 14 deletions(-) diff --git a/rlberry/agents/torch/ppo/ppo.py b/rlberry/agents/torch/ppo/ppo.py index 124a8c482..11b3e5f33 100644 --- a/rlberry/agents/torch/ppo/ppo.py +++ b/rlberry/agents/torch/ppo/ppo.py @@ -288,7 +288,11 @@ def fit(self, budget: int, lr_scheduler=None, **kwargs): if lr_scheduler is None: lr_scheduler = self._get_lr_scheduler(budget) - timesteps_counter = 0 + + if len(self.memory)==0: + timesteps_counter = 0 + else: #it's not the first "fit" on this agent, so there is a previous buffer to continue + timesteps_counter = len(self.memory)*self.n_envs episode_returns = np.zeros(self.n_envs, dtype=np.float32) episode_lengths = np.zeros(self.n_envs, dtype=np.int32) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 55f28e051..99d92c192 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -7,12 +7,15 @@ # ppo = PPOAgent(env) # ppo.fit(4096) -from rlberry.envs import Wrapper +from rlberry.envs import Wrapper, gym_make from rlberry.agents.torch import PPOAgent from rlberry.manager import AgentManager, evaluate_agents from rlberry.envs.benchmarks.ball_exploration import PBall2D from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env +import os +import pathlib +import shutil def test_ppo(): @@ -24,7 +27,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96), n_fit=1, @@ -44,7 +47,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96), n_fit=1, @@ -64,7 +67,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96), n_fit=1, @@ -78,11 +81,10 @@ def test_ppo(): env_ctor = PBall2D env_kwargs = dict() - pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96), n_fit=1, @@ -103,7 +105,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict( batch_size=24, @@ -133,7 +135,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict( batch_size=24, @@ -165,11 +167,10 @@ def test_ppo(): env_ctor = PBall2D env_kwargs = dict() - pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96), n_fit=1, @@ -184,7 +185,7 @@ def test_ppo(): pporlberry_stats = AgentManager( PPOAgent, (env_ctor, env_kwargs), - fit_budget=int(100), + fit_budget=int(1000), eval_kwargs=dict(eval_horizon=2), init_kwargs=dict(batch_size=24, n_steps=96, normalize_advantages=True), n_fit=1, @@ -197,5 +198,380 @@ def test_ppo(): pporlberry_stats.clear_output_dir() -if __name__ == "__main__": - test_ppo() +def test_ppo_single_env(): + env = gym_make("CartPole-v0") + agent = PPOAgent( + env, + learning_rate=1e-4, + optimizer_type="ADAM", + ) + agent.fit(budget=1000) + + saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step( + action + ) + if done: + next_observation = test_load_env.reset() + observation = next_observation + + os.remove(saving_path) + + + +def test_ppo_multi_env(): + env = gym_make("CartPole-v0") + agent = PPOAgent( + env, + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3 + ) + agent.fit(budget=1000) + + saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step( + action + ) + if done: + next_observation = test_load_env.reset() + observation = next_observation + + os.remove(saving_path) + + +def test_ppo_multi_fit(): + env = gym_make("CartPole-v0") + agent = PPOAgent( + env, + learning_rate=1e-4, + optimizer_type="ADAM", + ) + agent.fit(budget=1000) + agent.fit(budget=1000) + + saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step( + action + ) + if done: + next_observation = test_load_env.reset() + observation = next_observation + + os.remove(saving_path) + + +def test_ppo_multi_env_multi_fit(): + env = gym_make("CartPole-v0") + agent = PPOAgent( + env, + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3 + ) + agent.fit(budget=1000) + agent.fit(budget=1000) + + saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + # VRemove previous save + if os.path.exists(saving_path): + os.remove(saving_path) + assert not os.path.exists(saving_path) + + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent + + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step( + action + ) + if done: + next_observation = test_load_env.reset() + observation = next_observation + + os.remove(saving_path) + +def test_ppo_agent_manager_single_env(): + saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + ), + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) + + + +def test_ppo_agent_manager_multi_env(): + saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ) + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3, + ), + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) + + + +def test_ppo_agent_manager_multi_fit(): + saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + ), + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=1000) + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) + + +def test_ppo_agent_manager_multi_env_multi_fit(): + saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" + + # Remove previous save + if os.path.exists(saving_path): + shutil.rmtree(saving_path) + assert not os.path.exists(saving_path) + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ) + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3, + ), + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=saving_path, + ) + + test_agent_manager.fit(budget=1000) + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(saving_path) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + shutil.rmtree(saving_path) + From ae27dcc720b366a089710e07e3d5fc5beb01d29c Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 31 Mar 2023 13:49:33 +0000 Subject: [PATCH 40/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/ppo/ppo.py | 6 ++-- rlberry/agents/torch/tests/test_ppo.py | 39 ++++++-------------------- 2 files changed, 12 insertions(+), 33 deletions(-) diff --git a/rlberry/agents/torch/ppo/ppo.py b/rlberry/agents/torch/ppo/ppo.py index 11b3e5f33..99274cbf2 100644 --- a/rlberry/agents/torch/ppo/ppo.py +++ b/rlberry/agents/torch/ppo/ppo.py @@ -289,10 +289,10 @@ def fit(self, budget: int, lr_scheduler=None, **kwargs): if lr_scheduler is None: lr_scheduler = self._get_lr_scheduler(budget) - if len(self.memory)==0: + if len(self.memory) == 0: timesteps_counter = 0 - else: #it's not the first "fit" on this agent, so there is a previous buffer to continue - timesteps_counter = len(self.memory)*self.n_envs + else: # it's not the first "fit" on this agent, so there is a previous buffer to continue + timesteps_counter = len(self.memory) * self.n_envs episode_returns = np.zeros(self.n_envs, dtype=np.float32) episode_lengths = np.zeros(self.n_envs, dtype=np.int32) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 99d92c192..d741dd5d1 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -227,9 +227,7 @@ def test_ppo_single_env(): observation = test_load_env.reset() for tt in range(50): action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step( - action - ) + next_observation, reward, done, info = test_load_env.step(action) if done: next_observation = test_load_env.reset() observation = next_observation @@ -237,15 +235,9 @@ def test_ppo_single_env(): os.remove(saving_path) - def test_ppo_multi_env(): env = gym_make("CartPole-v0") - agent = PPOAgent( - env, - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3 - ) + agent = PPOAgent(env, learning_rate=1e-4, optimizer_type="ADAM", n_envs=3) agent.fit(budget=1000) saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" @@ -268,9 +260,7 @@ def test_ppo_multi_env(): observation = test_load_env.reset() for tt in range(50): action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step( - action - ) + next_observation, reward, done, info = test_load_env.step(action) if done: next_observation = test_load_env.reset() observation = next_observation @@ -308,9 +298,7 @@ def test_ppo_multi_fit(): observation = test_load_env.reset() for tt in range(50): action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step( - action - ) + next_observation, reward, done, info = test_load_env.step(action) if done: next_observation = test_load_env.reset() observation = next_observation @@ -320,12 +308,7 @@ def test_ppo_multi_fit(): def test_ppo_multi_env_multi_fit(): env = gym_make("CartPole-v0") - agent = PPOAgent( - env, - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3 - ) + agent = PPOAgent(env, learning_rate=1e-4, optimizer_type="ADAM", n_envs=3) agent.fit(budget=1000) agent.fit(budget=1000) @@ -349,15 +332,14 @@ def test_ppo_multi_env_multi_fit(): observation = test_load_env.reset() for tt in range(50): action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step( - action - ) + next_observation, reward, done, info = test_load_env.step(action) if done: next_observation = test_load_env.reset() observation = next_observation os.remove(saving_path) + def test_ppo_agent_manager_single_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -411,7 +393,6 @@ def test_ppo_agent_manager_single_env(): shutil.rmtree(saving_path) - def test_ppo_agent_manager_multi_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -426,7 +407,7 @@ def test_ppo_agent_manager_multi_env(): gym_make, dict( id="CartPole-v0", - ) + ), ), # The Environment to solve. init_kwargs=dict( # Where to put the agent's hyperparameters learning_rate=1e-4, @@ -466,7 +447,6 @@ def test_ppo_agent_manager_multi_env(): shutil.rmtree(saving_path) - def test_ppo_agent_manager_multi_fit(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -535,7 +515,7 @@ def test_ppo_agent_manager_multi_env_multi_fit(): gym_make, dict( id="CartPole-v0", - ) + ), ), # The Environment to solve. init_kwargs=dict( # Where to put the agent's hyperparameters learning_rate=1e-4, @@ -574,4 +554,3 @@ def test_ppo_agent_manager_multi_env_multi_fit(): state = next_s shutil.rmtree(saving_path) - From 8f71b96323e2295f175e47ff13d5dd026ce36c6f Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 16:04:31 +0200 Subject: [PATCH 41/65] xfail for windows... --- rlberry/agents/torch/tests/test_ppo.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index fcc1443cf..23df4a7ba 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -343,6 +343,7 @@ def test_ppo_multi_env_multi_fit(): os.remove(saving_path) +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_single_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -396,6 +397,7 @@ def test_ppo_agent_manager_single_env(): shutil.rmtree(saving_path) +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_env(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -450,6 +452,7 @@ def test_ppo_agent_manager_multi_env(): shutil.rmtree(saving_path) +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_fit(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" @@ -504,6 +507,7 @@ def test_ppo_agent_manager_multi_fit(): shutil.rmtree(saving_path) +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_env_multi_fit(): saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" From cb391a3fc94f096311227126d7fe6aa27d6d7da4 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 16:15:38 +0200 Subject: [PATCH 42/65] Empty-Commit From a36fadb4b12eeca7c74b28df588fda047db7bacf Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Fri, 31 Mar 2023 16:36:05 +0200 Subject: [PATCH 43/65] Empty-Commit From d0b91200b4db111a79f945158cb73f49e16be305 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Mon, 3 Apr 2023 14:55:40 +0200 Subject: [PATCH 44/65] remove 'sleep' (added for debug) --- rlberry/manager/tests/test_agent_manager.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index 407c1780d..ffe12204f 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -169,7 +169,6 @@ def test_agent_manager_2(): # test hyperparemeter optimization loaded_stats.optimize_hyperparams(n_trials=5) - time.sleep(3) # delete some writers stats_agent1.set_writer(1, None) stats_agent1.set_writer(2, None) From 1d6fd6116c99f4aacaa8f7eb882935c13db3637e Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Mon, 3 Apr 2023 14:58:17 +0200 Subject: [PATCH 45/65] remove sleep (old debug) --- rlberry/manager/tests/test_agent_manager.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index 407c1780d..ffe12204f 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -169,7 +169,6 @@ def test_agent_manager_2(): # test hyperparemeter optimization loaded_stats.optimize_hyperparams(n_trials=5) - time.sleep(3) # delete some writers stats_agent1.set_writer(1, None) stats_agent1.set_writer(2, None) From 7af5d3a534f2d6b5641fb8a6023b1500f513a323 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Mon, 3 Apr 2023 15:04:06 +0200 Subject: [PATCH 46/65] remove sleep (old debug) --- rlberry/manager/tests/test_agent_manager.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index ffe12204f..6aab6da4b 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -5,7 +5,6 @@ from rlberry.envs import GridWorld from rlberry.agents import AgentWithSimplePolicy from rlberry.manager import AgentManager, plot_writer_data, evaluate_agents -import time class DummyAgent(AgentWithSimplePolicy): From 98357b70f9386e93bac72e04b388694a5311d43e Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Mon, 3 Apr 2023 15:04:16 +0200 Subject: [PATCH 47/65] remove 'sleep' (added for debug) --- rlberry/manager/tests/test_agent_manager.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rlberry/manager/tests/test_agent_manager.py b/rlberry/manager/tests/test_agent_manager.py index ffe12204f..6aab6da4b 100644 --- a/rlberry/manager/tests/test_agent_manager.py +++ b/rlberry/manager/tests/test_agent_manager.py @@ -5,7 +5,6 @@ from rlberry.envs import GridWorld from rlberry.agents import AgentWithSimplePolicy from rlberry.manager import AgentManager, plot_writer_data, evaluate_agents -import time class DummyAgent(AgentWithSimplePolicy): From 35aa58b9ab5bec0b45ed3f9ff4437e2a6af26ba7 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Mon, 3 Apr 2023 17:13:26 +0200 Subject: [PATCH 48/65] use temporary folder instead --- rlberry/agents/torch/tests/test_ppo.py | 534 +++++++++++-------------- 1 file changed, 242 insertions(+), 292 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 23df4a7ba..6acd80708 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -18,6 +18,7 @@ import os import pathlib import shutil +import tempfile @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @@ -210,32 +211,25 @@ def test_ppo_single_env(): ) agent.fit(budget=1000) - saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + with tempfile.TemporaryDirectory() as tmpdirname: + saving_path = tmpdirname + "/agent.pickle" + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) - # VRemove previous save - if os.path.exists(saving_path): - os.remove(saving_path) - assert not os.path.exists(saving_path) + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - os.remove(saving_path) + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step(action) + if done: + next_observation = test_load_env.reset() + observation = next_observation def test_ppo_multi_env(): @@ -243,32 +237,25 @@ def test_ppo_multi_env(): agent = PPOAgent(env, learning_rate=1e-4, optimizer_type="ADAM", n_envs=3) agent.fit(budget=1000) - saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" - - # VRemove previous save - if os.path.exists(saving_path): - os.remove(saving_path) - assert not os.path.exists(saving_path) + with tempfile.TemporaryDirectory() as tmpdirname: + saving_path = tmpdirname + "/agent.pickle" + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - os.remove(saving_path) + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step(action) + if done: + next_observation = test_load_env.reset() + observation = next_observation def test_ppo_multi_fit(): @@ -281,32 +268,26 @@ def test_ppo_multi_fit(): agent.fit(budget=1000) agent.fit(budget=1000) - saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" - - # VRemove previous save - if os.path.exists(saving_path): - os.remove(saving_path) - assert not os.path.exists(saving_path) - - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) + with tempfile.TemporaryDirectory() as tmpdirname: + saving_path = tmpdirname + "/agent.pickle" + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step(action) + if done: + next_observation = test_load_env.reset() + observation = next_observation - os.remove(saving_path) def test_ppo_multi_env_multi_fit(): @@ -315,249 +296,218 @@ def test_ppo_multi_env_multi_fit(): agent.fit(budget=1000) agent.fit(budget=1000) - saving_path = "rlberry/agents/torch/tests/agent_test_ppo_classic_env.pickle" + + with tempfile.TemporaryDirectory() as tmpdirname: + saving_path = tmpdirname + "/agent.pickle" - # VRemove previous save - if os.path.exists(saving_path): - os.remove(saving_path) - assert not os.path.exists(saving_path) + # test the save function + agent.save(saving_path) + assert os.path.exists(saving_path) - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) + # test the loading function + test_load_env = gym_make("CartPole-v0") + loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) + assert loaded_agent - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - os.remove(saving_path) + # test the agent + observation = test_load_env.reset() + for tt in range(50): + action = loaded_agent.policy(observation) + next_observation, reward, done, info = test_load_env.step(action) + if done: + next_observation = test_load_env.reset() + observation = next_observation @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_single_env(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" - - # Remove previous save - if os.path.exists(saving_path): - shutil.rmtree(saving_path) - assert not os.path.exists(saving_path) - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", + + with tempfile.TemporaryDirectory() as tmpdirname: + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=saving_path, - ) - - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - shutil.rmtree(saving_path) + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=tmpdirname, + ) + + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(tmpdirname) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_env(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" - - # Remove previous save - if os.path.exists(saving_path): - shutil.rmtree(saving_path) - assert not os.path.exists(saving_path) - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", + with tempfile.TemporaryDirectory() as tmpdirname: + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3, ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3, - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=saving_path, - ) - - test_agent_manager.fit(budget=1000) + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=tmpdirname, + ) + + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(tmpdirname) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s - # test the save function - test_agent_manager.save() - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - shutil.rmtree(saving_path) @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_fit(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" - - # Remove previous save - if os.path.exists(saving_path): - shutil.rmtree(saving_path) - assert not os.path.exists(saving_path) - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", + with tempfile.TemporaryDirectory() as tmpdirname: + + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=saving_path, - ) - - test_agent_manager.fit(budget=1000) - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=tmpdirname, + ) + + test_agent_manager.fit(budget=1000) + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(tmpdirname) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - shutil.rmtree(saving_path) @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") def test_ppo_agent_manager_multi_env_multi_fit(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_ppo_classic_env" - - # Remove previous save - if os.path.exists(saving_path): - shutil.rmtree(saving_path) - assert not os.path.exists(saving_path) - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", + + with tempfile.TemporaryDirectory() as tmpdirname: + test_agent_manager = AgentManager( + PPOAgent, # The Agent class. + ( + gym_make, + dict( + id="CartPole-v0", + ), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + learning_rate=1e-4, + optimizer_type="ADAM", + n_envs=3, ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3, - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=saving_path, - ) - - test_agent_manager.fit(budget=1000) - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - shutil.rmtree(saving_path) + fit_budget=1000, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=50 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="test_ppo_classic_env", # The agent's name. + output_dir=tmpdirname, + ) + + test_agent_manager.fit(budget=1000) + test_agent_manager.fit(budget=1000) + + # test the save function + test_agent_manager.save() + assert os.path.exists(tmpdirname) + + # test the loading function + test_load_env = gym_make("CartPole-v0") + path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test the agent + state = test_load_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s From 02e69b1f8c28257567fa39d8423faa131fbaf571 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Tue, 4 Apr 2023 16:20:29 +0200 Subject: [PATCH 49/65] generalize PPO tests to 'check_agent' --- rlberry/agents/torch/tests/test_ppo.py | 315 ------------------------- rlberry/tests/test_agent.py | 10 +- rlberry/utils/check_agent.py | 134 ++++++++++- 3 files changed, 134 insertions(+), 325 deletions(-) diff --git a/rlberry/agents/torch/tests/test_ppo.py b/rlberry/agents/torch/tests/test_ppo.py index 6acd80708..766849b70 100644 --- a/rlberry/agents/torch/tests/test_ppo.py +++ b/rlberry/agents/torch/tests/test_ppo.py @@ -15,10 +15,6 @@ from gym import make from rlberry.agents.torch.utils.training import model_factory_from_env import sys -import os -import pathlib -import shutil -import tempfile @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @@ -200,314 +196,3 @@ def test_ppo(): output = evaluate_agents([pporlberry_stats], n_simulations=2, plot=False) pporlberry_stats.clear_output_dir() - - -def test_ppo_single_env(): - env = gym_make("CartPole-v0") - agent = PPOAgent( - env, - learning_rate=1e-4, - optimizer_type="ADAM", - ) - agent.fit(budget=1000) - - with tempfile.TemporaryDirectory() as tmpdirname: - saving_path = tmpdirname + "/agent.pickle" - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - -def test_ppo_multi_env(): - env = gym_make("CartPole-v0") - agent = PPOAgent(env, learning_rate=1e-4, optimizer_type="ADAM", n_envs=3) - agent.fit(budget=1000) - - with tempfile.TemporaryDirectory() as tmpdirname: - saving_path = tmpdirname + "/agent.pickle" - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - -def test_ppo_multi_fit(): - env = gym_make("CartPole-v0") - agent = PPOAgent( - env, - learning_rate=1e-4, - optimizer_type="ADAM", - ) - agent.fit(budget=1000) - agent.fit(budget=1000) - - with tempfile.TemporaryDirectory() as tmpdirname: - saving_path = tmpdirname + "/agent.pickle" - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - - -def test_ppo_multi_env_multi_fit(): - env = gym_make("CartPole-v0") - agent = PPOAgent(env, learning_rate=1e-4, optimizer_type="ADAM", n_envs=3) - agent.fit(budget=1000) - agent.fit(budget=1000) - - - with tempfile.TemporaryDirectory() as tmpdirname: - saving_path = tmpdirname + "/agent.pickle" - - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = PPOAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - observation = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(observation) - next_observation, reward, done, info = test_load_env.step(action) - if done: - next_observation = test_load_env.reset() - observation = next_observation - - -@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") -def test_ppo_agent_manager_single_env(): - - with tempfile.TemporaryDirectory() as tmpdirname: - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", - ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=tmpdirname, - ) - - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(tmpdirname) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - -@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") -def test_ppo_agent_manager_multi_env(): - with tempfile.TemporaryDirectory() as tmpdirname: - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", - ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3, - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=tmpdirname, - ) - - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(tmpdirname) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - - -@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") -def test_ppo_agent_manager_multi_fit(): - with tempfile.TemporaryDirectory() as tmpdirname: - - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", - ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=tmpdirname, - ) - - test_agent_manager.fit(budget=1000) - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(tmpdirname) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - - -@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") -def test_ppo_agent_manager_multi_env_multi_fit(): - - with tempfile.TemporaryDirectory() as tmpdirname: - test_agent_manager = AgentManager( - PPOAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", - ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_rate=1e-4, - optimizer_type="ADAM", - n_envs=3, - ), - fit_budget=1000, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_ppo_classic_env", # The agent's name. - output_dir=tmpdirname, - ) - - test_agent_manager.fit(budget=1000) - test_agent_manager.fit(budget=1000) - - # test the save function - test_agent_manager.save() - assert os.path.exists(tmpdirname) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s diff --git a/rlberry/tests/test_agent.py b/rlberry/tests/test_agent.py index ae8727482..9ff28f95a 100644 --- a/rlberry/tests/test_agent.py +++ b/rlberry/tests/test_agent.py @@ -2,7 +2,7 @@ import rlberry.agents as agents import rlberry.agents.torch as torch_agents from rlberry.agents.experimental import torch as torch_exp_agents -from rlberry.utils.check_agent import check_rl_agent, check_rlberry_agent +from rlberry.utils.check_agent import check_rl_agent, check_rlberry_agent, check_vectorized_env_agent from rlberry.agents.features import FeatureMap import numpy as np import sys @@ -51,6 +51,9 @@ def feature_map_fn(_env): torch_exp_agents.AVECPPOAgent, ] +MULTI_ENV_AGENTS= [ + torch_agents.PPOAgent, +] @pytest.mark.parametrize("agent", FINITE_MDP_AGENTS) def test_finite_state_agent(agent): @@ -63,3 +66,8 @@ def test_finite_state_agent(agent): def test_continuous_state_agent(agent): check_rl_agent(agent, env="continuous_state") check_rlberry_agent(agent, env="continuous_state") + +@pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") +@pytest.mark.parametrize("agent", MULTI_ENV_AGENTS) +def test_continuous_vectorized_env_agent(agent): + check_vectorized_env_agent(agent, env="vectorized_env_continuous") \ No newline at end of file diff --git a/rlberry/utils/check_agent.py b/rlberry/utils/check_agent.py index 0057dc633..0c1b894d8 100644 --- a/rlberry/utils/check_agent.py +++ b/rlberry/utils/check_agent.py @@ -5,6 +5,9 @@ from rlberry.seeding import set_external_seed import tempfile import os +from rlberry.envs.gym_make import gym_make +import pathlib + SEED = 42 @@ -20,6 +23,9 @@ def _make_env(env): elif env == "discrete_state": env_ctor = Chain env_kwargs = {} + elif env == "vectorized_env_continuous": + env_ctor = gym_make + env_kwargs = dict(id="CartPole-v0") else: raise ValueError("The env given in parameter is not implemented") elif isinstance(env, tuple): @@ -176,27 +182,48 @@ def check_save_load(agent, env="continuous_state", init_kwargs=None): if init_kwargs is None: init_kwargs = {} - train_env = _make_env(env) - env = train_env[0](**train_env[1]) + train_env_tuple = _make_env(env) with tempfile.TemporaryDirectory() as tmpdirname: - agent = AgentManager( + manager = AgentManager( agent, - train_env, + train_env_tuple, fit_budget=5, n_fit=1, seed=SEED, init_kwargs=init_kwargs, output_dir=tmpdirname, ) - agent.fit(3) + train_env = manager.train_env + test_env = train_env_tuple[0](**train_env_tuple[1]) + + manager.fit(3) + + #test individual agents save and load assert ( - os.path.getsize(str(agent.output_dir_) + "/agent_handlers/idx_0.pickle") > 1 + os.path.getsize(str(manager.output_dir_) + "/agent_handlers/idx_0.pickle") > 1 ), "The saved file is empty." try: - agent.load(str(agent.output_dir_) + "/agent_handlers/idx_0.pickle") + manager.load(str(manager.output_dir_) + "/agent_handlers/idx_0.pickle") except Exception: raise RuntimeError("Failed to load the agent file.") - + + #test agentManager save and load + manager.save() + assert os.path.exists(tmpdirname) + + path_to_load = next(pathlib.Path(tmpdirname).glob("**/*.pickle")) + loaded_agent_manager = AgentManager.load(path_to_load) + assert loaded_agent_manager + + # test with firest agent of the manager + observation = test_env.reset() + for tt in range(50): + action = loaded_agent_manager.get_agent_instances()[0].policy(observation) + next_observation, reward, done, info = test_env.step(action) + if done: + next_observation = test_env.reset() + observation = next_observation + def check_seeding_agent(agent, env=None, continuous_state=False, init_kwargs=None): """ @@ -222,6 +249,95 @@ def check_seeding_agent(agent, env=None, continuous_state=False, init_kwargs=Non assert result, "Agent not reproducible (same seed give different results)" +def check_multi_fit (agent, env="continuous_state", init_kwargs=None): + """ + Check that fitting two times with budget greater than n_step (buffer size) is working. + + Parameters + ---------- + agent: rlberry agent module + Agent class to test. + env: tuple (env_ctor, env_kwargs) or str in ["continuous_state", "discrete_state"], default="continuous_state" + if tuple, env is the constructor and keywords of the env on which to test. + if str in ["continuous_state", "discrete_state"], we use a default Benchmark environment. + init_kwargs : dict + Arguments required by the agent's constructor. + """ + if init_kwargs is None: + init_kwargs = {} + + + init_kwargs["seeder"] = SEED + train_env_d = _make_env(env) + train_env = train_env_d[0](**train_env_d[1]) + + test_load_env_d = _make_env(env) + test_load_env = test_load_env_d[0](**test_load_env_d[1]) + + agent1 = agent(train_env, **init_kwargs) + + + if "n_steps" in agent1.get_params(): + agent1.n_steps = 30 + + agent1.fit(100) + agent1.fit(100) + + # test + state = test_load_env.reset() + for tt in range(50): + action = agent1.policy(state) + next_s, _, done, test = test_load_env.step(action) + if done: + break + state = next_s + + +def check_vectorized_env_agent(agent,env="vectorized_env_continuous", agent_init_kwargs=None): + """ + Check that (multi-)fitting vectorized_env is working. + + Parameters + ---------- + agent: rlberry agent module + Agent class to test. + env: tuple (env_ctor, env_kwargs) or str in {"continuous_state", "discrete_state","vectorized"}, default="vectorized_env_continuous" + if tuple, env is the constructor and keywords of the env on which to test. + if str in {"continuous_state", "discrete_state","vectorized"}, we use a default Benchmark environment. + agent_init_kwargs : dict + Arguments required by the agent's constructor. + """ + + if agent_init_kwargs is None: + agent_init_kwargs = dict(learning_rate=1e-4, optimizer_type="ADAM", n_envs=3, n_steps=30) + + + if "n_steps" not in agent_init_kwargs or agent_init_kwargs["n_envs"] is None: + agent_init_kwargs["n_envs"] = 3 + if "n_steps" not in agent_init_kwargs or agent_init_kwargs["n_steps"] is None: + agent_init_kwargs["n_steps"] = 30 + + agent_init_kwargs["seeder"] = SEED + + + env_d = _make_env(env) + train_env = env_d[0](**env_d[1]) + test_env = env_d[0](**env_d[1]) + + agent1 = agent(train_env, **agent_init_kwargs) + agent1.fit(100) + agent1.fit(100) + + # test the agent + state = test_env.reset() + for tt in range(50): + action = agent1.policy(state) + next_s, _, done, test = test_env.step(action) + if done: + break + state = next_s + + def check_rl_agent(agent, env="continuous_state", init_kwargs=None): """ Check agent manager compatibility and check reproducibility/seeding. @@ -249,7 +365,7 @@ def check_rl_agent(agent, env="continuous_state", init_kwargs=None): check_seeding_agent(agent, env, init_kwargs=init_kwargs) # check reproducibility check_fit_additive(agent, env, init_kwargs=init_kwargs) check_save_load(agent, env, init_kwargs=init_kwargs) - + check_multi_fit(agent, env, init_kwargs=init_kwargs) def check_rlberry_agent(agent, env="continuous_state", init_kwargs=None): """ From 90733538ff450b42ca5bfe28130bd78b4a7a816b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Apr 2023 14:20:54 +0000 Subject: [PATCH 50/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/tests/test_agent.py | 12 +++++++++--- rlberry/utils/check_agent.py | 32 +++++++++++++++++--------------- 2 files changed, 26 insertions(+), 18 deletions(-) diff --git a/rlberry/tests/test_agent.py b/rlberry/tests/test_agent.py index 9ff28f95a..cac41aec5 100644 --- a/rlberry/tests/test_agent.py +++ b/rlberry/tests/test_agent.py @@ -2,7 +2,11 @@ import rlberry.agents as agents import rlberry.agents.torch as torch_agents from rlberry.agents.experimental import torch as torch_exp_agents -from rlberry.utils.check_agent import check_rl_agent, check_rlberry_agent, check_vectorized_env_agent +from rlberry.utils.check_agent import ( + check_rl_agent, + check_rlberry_agent, + check_vectorized_env_agent, +) from rlberry.agents.features import FeatureMap import numpy as np import sys @@ -51,10 +55,11 @@ def feature_map_fn(_env): torch_exp_agents.AVECPPOAgent, ] -MULTI_ENV_AGENTS= [ +MULTI_ENV_AGENTS = [ torch_agents.PPOAgent, ] + @pytest.mark.parametrize("agent", FINITE_MDP_AGENTS) def test_finite_state_agent(agent): check_rl_agent(agent, env="discrete_state") @@ -67,7 +72,8 @@ def test_continuous_state_agent(agent): check_rl_agent(agent, env="continuous_state") check_rlberry_agent(agent, env="continuous_state") + @pytest.mark.xfail(sys.platform == "win32", reason="bug with windows???") @pytest.mark.parametrize("agent", MULTI_ENV_AGENTS) def test_continuous_vectorized_env_agent(agent): - check_vectorized_env_agent(agent, env="vectorized_env_continuous") \ No newline at end of file + check_vectorized_env_agent(agent, env="vectorized_env_continuous") diff --git a/rlberry/utils/check_agent.py b/rlberry/utils/check_agent.py index 0c1b894d8..c74cc5bf7 100644 --- a/rlberry/utils/check_agent.py +++ b/rlberry/utils/check_agent.py @@ -25,7 +25,7 @@ def _make_env(env): env_kwargs = {} elif env == "vectorized_env_continuous": env_ctor = gym_make - env_kwargs = dict(id="CartPole-v0") + env_kwargs = dict(id="CartPole-v0") else: raise ValueError("The env given in parameter is not implemented") elif isinstance(env, tuple): @@ -198,16 +198,17 @@ def check_save_load(agent, env="continuous_state", init_kwargs=None): manager.fit(3) - #test individual agents save and load + # test individual agents save and load assert ( - os.path.getsize(str(manager.output_dir_) + "/agent_handlers/idx_0.pickle") > 1 + os.path.getsize(str(manager.output_dir_) + "/agent_handlers/idx_0.pickle") + > 1 ), "The saved file is empty." try: manager.load(str(manager.output_dir_) + "/agent_handlers/idx_0.pickle") except Exception: raise RuntimeError("Failed to load the agent file.") - - #test agentManager save and load + + # test agentManager save and load manager.save() assert os.path.exists(tmpdirname) @@ -223,7 +224,7 @@ def check_save_load(agent, env="continuous_state", init_kwargs=None): if done: next_observation = test_env.reset() observation = next_observation - + def check_seeding_agent(agent, env=None, continuous_state=False, init_kwargs=None): """ @@ -249,7 +250,7 @@ def check_seeding_agent(agent, env=None, continuous_state=False, init_kwargs=Non assert result, "Agent not reproducible (same seed give different results)" -def check_multi_fit (agent, env="continuous_state", init_kwargs=None): +def check_multi_fit(agent, env="continuous_state", init_kwargs=None): """ Check that fitting two times with budget greater than n_step (buffer size) is working. @@ -266,7 +267,6 @@ def check_multi_fit (agent, env="continuous_state", init_kwargs=None): if init_kwargs is None: init_kwargs = {} - init_kwargs["seeder"] = SEED train_env_d = _make_env(env) train_env = train_env_d[0](**train_env_d[1]) @@ -276,7 +276,6 @@ def check_multi_fit (agent, env="continuous_state", init_kwargs=None): agent1 = agent(train_env, **init_kwargs) - if "n_steps" in agent1.get_params(): agent1.n_steps = 30 @@ -293,7 +292,9 @@ def check_multi_fit (agent, env="continuous_state", init_kwargs=None): state = next_s -def check_vectorized_env_agent(agent,env="vectorized_env_continuous", agent_init_kwargs=None): +def check_vectorized_env_agent( + agent, env="vectorized_env_continuous", agent_init_kwargs=None +): """ Check that (multi-)fitting vectorized_env is working. @@ -309,8 +310,9 @@ def check_vectorized_env_agent(agent,env="vectorized_env_continuous", agent_init """ if agent_init_kwargs is None: - agent_init_kwargs = dict(learning_rate=1e-4, optimizer_type="ADAM", n_envs=3, n_steps=30) - + agent_init_kwargs = dict( + learning_rate=1e-4, optimizer_type="ADAM", n_envs=3, n_steps=30 + ) if "n_steps" not in agent_init_kwargs or agent_init_kwargs["n_envs"] is None: agent_init_kwargs["n_envs"] = 3 @@ -318,16 +320,15 @@ def check_vectorized_env_agent(agent,env="vectorized_env_continuous", agent_init agent_init_kwargs["n_steps"] = 30 agent_init_kwargs["seeder"] = SEED - env_d = _make_env(env) train_env = env_d[0](**env_d[1]) test_env = env_d[0](**env_d[1]) - + agent1 = agent(train_env, **agent_init_kwargs) agent1.fit(100) agent1.fit(100) - + # test the agent state = test_env.reset() for tt in range(50): @@ -367,6 +368,7 @@ def check_rl_agent(agent, env="continuous_state", init_kwargs=None): check_save_load(agent, env, init_kwargs=init_kwargs) check_multi_fit(agent, env, init_kwargs=init_kwargs) + def check_rlberry_agent(agent, env="continuous_state", init_kwargs=None): """ Companion to check_rl_agent, contains additional tests. 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z$EnMOFO6bx6&y$jXN_NbRE1KT0m>t*LU0ty_y-Ua4`>>#-s)mJnL7l9R}8~2~joK*#SBKiif4o9Kc7Ws&T>!bp2xegz=9GO-W{veJ4 zx%6;BZLm)Y4pMy$rNbw`w4cmrJ@_G?a^JXgXVIpeKXc^GX3x$#`0Y*LG}!+v1P5s@ z1rhmk#s`!B^ Date: Tue, 4 Apr 2023 16:48:11 +0200 Subject: [PATCH 52/65] patch : stableBaselines don't have get_params() --- rlberry/utils/check_agent.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/rlberry/utils/check_agent.py b/rlberry/utils/check_agent.py index c74cc5bf7..b066541fe 100644 --- a/rlberry/utils/check_agent.py +++ b/rlberry/utils/check_agent.py @@ -276,8 +276,12 @@ def check_multi_fit(agent, env="continuous_state", init_kwargs=None): agent1 = agent(train_env, **init_kwargs) - if "n_steps" in agent1.get_params(): - agent1.n_steps = 30 + try: # stableBaselines don't have get_params() + if "n_steps" in agent1.get_params(): + agent1.n_steps = 30 + except: + pass + agent1.fit(100) agent1.fit(100) From 43774314e52992dbdd60c3f2c8cca71853a722c6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 4 Apr 2023 14:48:32 +0000 Subject: [PATCH 53/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/utils/check_agent.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/rlberry/utils/check_agent.py b/rlberry/utils/check_agent.py index b066541fe..d5a4bb95d 100644 --- a/rlberry/utils/check_agent.py +++ b/rlberry/utils/check_agent.py @@ -276,13 +276,12 @@ def check_multi_fit(agent, env="continuous_state", init_kwargs=None): agent1 = agent(train_env, **init_kwargs) - try: # stableBaselines don't have get_params() + try: # stableBaselines don't have get_params() if "n_steps" in agent1.get_params(): - agent1.n_steps = 30 + agent1.n_steps = 30 except: pass - agent1.fit(100) agent1.fit(100) From ef0d9176079ad9f981a1d16336b6b557ead9041f Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Tue, 4 Apr 2023 17:02:17 +0200 Subject: [PATCH 54/65] Empty-Commit From e8f0b298cef1d9b0978e5b032214cd221be1e7df Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 5 Apr 2023 09:08:29 +0200 Subject: [PATCH 55/65] update doc --- rlberry/utils/check_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rlberry/utils/check_agent.py b/rlberry/utils/check_agent.py index d5a4bb95d..52fe1d230 100644 --- a/rlberry/utils/check_agent.py +++ b/rlberry/utils/check_agent.py @@ -305,7 +305,7 @@ def check_vectorized_env_agent( ---------- agent: rlberry agent module Agent class to test. - env: tuple (env_ctor, env_kwargs) or str in {"continuous_state", "discrete_state","vectorized"}, default="vectorized_env_continuous" + env: tuple (env_ctor, env_kwargs) or str "vectorized_env_continuous (default="vectorized_env_continuous") if tuple, env is the constructor and keywords of the env on which to test. if str in {"continuous_state", "discrete_state","vectorized"}, we use a default Benchmark environment. agent_init_kwargs : dict From 3789caf2667b5855f7199219a18978112ead0720 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 5 Apr 2023 09:29:13 +0200 Subject: [PATCH 56/65] Empty-Commit From d310dd232e7225144db4161f03110232cc94c570 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 5 Apr 2023 09:48:31 +0200 Subject: [PATCH 57/65] don't remove PyOpenGL_accelerate --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 67a4a71de..1ea004bac 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,7 @@ numba optuna ffmpeg-python PyOpenGL==3.1.5 +PyOpenGL_accelerate==3.1.5 pyvirtualdisplay torch>=1.6.0 stable-baselines3 From 03afc5c5b55faee7ad61eeaeca93514dadc50161 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 5 Apr 2023 14:30:40 +0200 Subject: [PATCH 58/65] add tests for atari empty input dim --- .../agents/torch/tests/test_torch_atari.py | 102 ++++++++++++++++++ .../agents/torch/tests/test_torch_models.py | 51 --------- rlberry/agents/torch/utils/training.py | 4 +- 3 files changed, 104 insertions(+), 53 deletions(-) create mode 100644 rlberry/agents/torch/tests/test_torch_atari.py diff --git a/rlberry/agents/torch/tests/test_torch_atari.py b/rlberry/agents/torch/tests/test_torch_atari.py new file mode 100644 index 000000000..ac8742cdb --- /dev/null +++ b/rlberry/agents/torch/tests/test_torch_atari.py @@ -0,0 +1,102 @@ +from rlberry.manager.agent_manager import AgentManager +from rlberry.agents.torch.dqn.dqn import DQNAgent +from rlberry.envs.gym_make import atari_make + + + +def test_forward_dqn(): + mlp_configs = { + "type": "MultiLayerPerceptron", # A network architecture + "layer_sizes": [512], # Network dimensions + "reshape": False, + "is_policy": False, # The network should output a distribution + # over actions + } + + cnn_configs = { + "type": "ConvolutionalNetwork", # A network architecture + "activation": "RELU", + "in_channels": 4, + "in_height": 84, + "in_width": 84, + "head_mlp_kwargs": mlp_configs, + "transpose_obs": False, + "is_policy": False, # The network should output a distribution + } + + tuned_agent = AgentManager( + DQNAgent, # The Agent class. + ( + atari_make, + # uncomment when rlberry will manage vectorized env + # dict(id="ALE/Breakout-v5", n_envs=3), + dict(id="ALE/Breakout-v5", n_envs=1), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + q_net_constructor="rlberry.agents.torch.utils.training.model_factory_from_env", + q_net_kwargs=cnn_configs, + max_replay_size=100, + batch_size=32, + learning_starts=100, + gradient_steps=1, + epsilon_final=0.01, + learning_rate=1e-4, # Size of the policy gradient descent steps. + chunk_size=5, + ), + fit_budget=200, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=10 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="DQN_test", # The agent's name. + ) + + tuned_agent.fit() + + + +def test_forward_empty_input_dim(): + mlp_configs = { + "type": "MultiLayerPerceptron", # A network architecture + "layer_sizes": [512], # Network dimensions + "reshape": False, + "is_policy": False, # The network should output a distribution + # over actions + } + + cnn_configs = { + "type": "ConvolutionalNetwork", # A network architecture + "activation": "RELU", + "head_mlp_kwargs": mlp_configs, + "transpose_obs": False, + "is_policy": False, # The network should output a distribution + } + + tuned_agent = AgentManager( + DQNAgent, # The Agent class. + ( + atari_make, + # uncomment when rlberry will manage vectorized env + # dict(id="ALE/Breakout-v5", n_envs=3), + dict(id="ALE/Breakout-v5", n_envs=1), + ), # The Environment to solve. + init_kwargs=dict( # Where to put the agent's hyperparameters + q_net_constructor="rlberry.agents.torch.utils.training.model_factory_from_env", + q_net_kwargs=cnn_configs, + max_replay_size=100, + batch_size=32, + learning_starts=100, + gradient_steps=1, + epsilon_final=0.01, + learning_rate=1e-4, # Size of the policy gradient descent steps. + chunk_size=5, + ), + fit_budget=10, # The number of interactions between the agent and the environment during training. + eval_kwargs=dict( + eval_horizon=10 + ), # The number of interactions between the agent and the environment during evaluations. + n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. + agent_name="DQN_test", # The agent's name. + ) + + tuned_agent.fit() diff --git a/rlberry/agents/torch/tests/test_torch_models.py b/rlberry/agents/torch/tests/test_torch_models.py index 55ebddcf7..a43a520a3 100644 --- a/rlberry/agents/torch/tests/test_torch_models.py +++ b/rlberry/agents/torch/tests/test_torch_models.py @@ -7,9 +7,6 @@ from rlberry.agents.torch.utils.models import ConvolutionalNetwork, DuelingNetwork from rlberry.agents.torch.utils.attention_models import EgoAttention from rlberry.agents.torch.utils.attention_models import SelfAttention -from rlberry.manager.agent_manager import AgentManager -from rlberry.agents.torch.dqn.dqn import DQNAgent -from rlberry.envs.gym_make import atari_make def test_mlp(): @@ -59,51 +56,3 @@ def test_ego_attention(): def test_self_attention(): _ = SelfAttention() - - -def test_forward_atari_dqn(): - mlp_configs = { - "type": "MultiLayerPerceptron", # A network architecture - "layer_sizes": [512], # Network dimensions - "reshape": False, - "is_policy": False, # The network should output a distribution - # over actions - } - - cnn_configs = { - "type": "ConvolutionalNetwork", # A network architecture - "activation": "RELU", - "in_channels": 4, - "in_height": 84, - "in_width": 84, - "head_mlp_kwargs": mlp_configs, - "transpose_obs": False, - "is_policy": False, # The network should output a distribution - } - - tuned_agent = AgentManager( - DQNAgent, # The Agent class. - ( - atari_make, - dict(id="ALE/Breakout-v5", n_envs=3), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - q_net_constructor="rlberry.agents.torch.utils.training.model_factory_from_env", - q_net_kwargs=cnn_configs, - max_replay_size=100, - batch_size=32, - learning_starts=100, - gradient_steps=1, - epsilon_final=0.01, - learning_rate=1e-4, # Size of the policy gradient descent steps. - chunk_size=5, - ), - fit_budget=200, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=10 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="DQN_test", # The agent's name. - ) - - tuned_agent.fit() diff --git a/rlberry/agents/torch/utils/training.py b/rlberry/agents/torch/utils/training.py index eb62d1a8f..01345c431 100644 --- a/rlberry/agents/torch/utils/training.py +++ b/rlberry/agents/torch/utils/training.py @@ -111,8 +111,8 @@ def size_model_config(env, **model_config): return model_config # Assume CHW observation space - if model_config["type"] == "ConvolutionalNetwork": - if not model_config["transpose_obs"]: + if "type" in model_config and model_config["type"] == "ConvolutionalNetwork": + if "transpose_obs" in model_config and not model_config["transpose_obs"]: # Assume CHW observation space if "in_channels" not in model_config: model_config["in_channels"] = int(obs_shape[0]) From d321a99600735b82226401ec08f886506135f954 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 5 Apr 2023 12:31:01 +0000 Subject: [PATCH 59/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/tests/test_torch_atari.py | 2 -- rlberry/agents/torch/utils/training.py | 2 +- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/rlberry/agents/torch/tests/test_torch_atari.py b/rlberry/agents/torch/tests/test_torch_atari.py index ac8742cdb..0d29b33ad 100644 --- a/rlberry/agents/torch/tests/test_torch_atari.py +++ b/rlberry/agents/torch/tests/test_torch_atari.py @@ -3,7 +3,6 @@ from rlberry.envs.gym_make import atari_make - def test_forward_dqn(): mlp_configs = { "type": "MultiLayerPerceptron", # A network architecture @@ -54,7 +53,6 @@ def test_forward_dqn(): tuned_agent.fit() - def test_forward_empty_input_dim(): mlp_configs = { "type": "MultiLayerPerceptron", # A network architecture diff --git a/rlberry/agents/torch/utils/training.py b/rlberry/agents/torch/utils/training.py index 01345c431..bf76771ad 100644 --- a/rlberry/agents/torch/utils/training.py +++ b/rlberry/agents/torch/utils/training.py @@ -111,7 +111,7 @@ def size_model_config(env, **model_config): return model_config # Assume CHW observation space - if "type" in model_config and model_config["type"] == "ConvolutionalNetwork": + if "type" in model_config and model_config["type"] == "ConvolutionalNetwork": if "transpose_obs" in model_config and not model_config["transpose_obs"]: # Assume CHW observation space if "in_channels" not in model_config: From fad2801796406724fc49818037d19fd209549ef5 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 5 Apr 2023 15:10:37 +0200 Subject: [PATCH 60/65] remove test (already exist in "check_agent.py") --- rlberry/agents/torch/tests/test_dqn.py | 105 ------------------------- 1 file changed, 105 deletions(-) diff --git a/rlberry/agents/torch/tests/test_dqn.py b/rlberry/agents/torch/tests/test_dqn.py index 14c12b20f..ed7af84f9 100644 --- a/rlberry/agents/torch/tests/test_dqn.py +++ b/rlberry/agents/torch/tests/test_dqn.py @@ -38,108 +38,3 @@ def mlp(env, **kwargs): env, q_net_constructor=mlp, q_net_kwargs=model_configs, learning_starts=100 ) new_agent.fit(budget=2000) - - -import os -from rlberry.manager.agent_manager import AgentManager -import shutil -import pathlib - - -def test_dqn_classic_env(): - env = gym_make("CartPole-v0") - agent = DQNAgent( - env, - learning_starts=5, - eval_interval=75, - train_interval=2, - gradient_steps=-1, - use_double_dqn=True, - use_prioritized_replay=True, - ) - agent.fit(budget=200) - - saving_path = "rlberry/agents/torch/tests/agent_test_dqn_classic_env.pickle" - - # VRemove previous save - if os.path.exists(saving_path): - os.remove(saving_path) - assert not os.path.exists(saving_path) - - # test the save function - agent.save(saving_path) - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - loaded_agent = DQNAgent.load(saving_path, **dict(env=test_load_env)) - assert loaded_agent - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent.policy(state) - next_state, reward, done, _ = test_load_env.step(action) - if done: - next_state = test_load_env.reset() - state = next_state - - os.remove(saving_path) - - -def test_dqn_agent_manager_classic_env(): - saving_path = "rlberry/agents/torch/tests/agentmanager_test_dqn_classic_env" - - # Remove previous save - if os.path.exists(saving_path): - shutil.rmtree(saving_path) - assert not os.path.exists(saving_path) - - test_agent_manager = AgentManager( - DQNAgent, # The Agent class. - ( - gym_make, - dict( - id="CartPole-v0", - ), - ), # The Environment to solve. - init_kwargs=dict( # Where to put the agent's hyperparameters - learning_starts=5, - eval_interval=75, - train_interval=2, - gradient_steps=-1, - use_double_dqn=True, - use_prioritized_replay=True, - chunk_size=1, - ), - fit_budget=200, # The number of interactions between the agent and the environment during training. - eval_kwargs=dict( - eval_horizon=50 - ), # The number of interactions between the agent and the environment during evaluations. - n_fit=1, # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic. - agent_name="test_dqn_classic_env", # The agent's name. - output_dir=saving_path, - ) - - test_agent_manager.fit(budget=200) - - # test the save function - test_agent_manager.save() - assert os.path.exists(saving_path) - - # test the loading function - test_load_env = gym_make("CartPole-v0") - path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle")) - loaded_agent_manager = AgentManager.load(path_to_load) - assert loaded_agent_manager - - # test the agent - state = test_load_env.reset() - for tt in range(50): - action = loaded_agent_manager.get_agent_instances()[0].policy(state) - next_s, _, done, test = test_load_env.step(action) - if done: - break - state = next_s - - shutil.rmtree(saving_path) From 0c267944b80704b5dcd0b371f2e3945476554a52 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Tue, 11 Apr 2023 09:10:50 +0200 Subject: [PATCH 61/65] updades following Matheus review --- rlberry/agents/torch/utils/models.py | 5 ++--- rlberry/envs/gym_make.py | 7 +++---- 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/rlberry/agents/torch/utils/models.py b/rlberry/agents/torch/utils/models.py index 9a9f5606a..5f6e96b23 100644 --- a/rlberry/agents/torch/utils/models.py +++ b/rlberry/agents/torch/utils/models.py @@ -459,9 +459,8 @@ def _get_conv_out_size(self, shape): def convolutions(self, x): x = x.float() - if ( - len(x.shape) == 3 - ): # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) + # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) + if (len(x.shape) == 3): x = x.unsqueeze(0) if self.transpose_obs: x = torch.transpose(x, -1, -3) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index a12ecbdc8..b01ecfd6e 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -33,7 +33,7 @@ def gym_make(id, wrap_spaces=False, **kwargs): return Wrapper(env, wrap_spaces=wrap_spaces) -def atari_make(id, scalarize=None, **kwargs): +def atari_make(id, scalarize=False, **kwargs): from stable_baselines3.common.env_util import make_atari_env from stable_baselines3.common.vec_env import VecFrameStack @@ -44,11 +44,10 @@ def atari_make(id, scalarize=None, **kwargs): # else: # scalarize = True - scalarize = True + scalarize = True #TODO : to remove with th PR :[WIP] Atari part2 (https://github.com/rlberry-py/rlberry/pull/285) if "atari_wrappers_dict" in kwargs.keys(): - atari_wrappers_dict = kwargs["atari_wrappers_dict"] - kwargs.pop("atari_wrappers_dict", None) + atari_wrappers_dict = kwargs.pop("atari_wrappers_dict") else: atari_wrappers_dict = dict( terminal_on_life_loss=False From 46ad222da910a353be3ef35444f6f2141a110155 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Apr 2023 07:11:16 +0000 Subject: [PATCH 62/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/agents/torch/utils/models.py | 2 +- rlberry/envs/gym_make.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/rlberry/agents/torch/utils/models.py b/rlberry/agents/torch/utils/models.py index 5f6e96b23..bd6ee94ea 100644 --- a/rlberry/agents/torch/utils/models.py +++ b/rlberry/agents/torch/utils/models.py @@ -460,7 +460,7 @@ def _get_conv_out_size(self, shape): def convolutions(self, x): x = x.float() # if there is no batch (CHW), add one dimension to specify batch of 1 (and get format BCHW) - if (len(x.shape) == 3): + if len(x.shape) == 3: x = x.unsqueeze(0) if self.transpose_obs: x = torch.transpose(x, -1, -3) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index b01ecfd6e..b8a05c7cc 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -44,7 +44,7 @@ def atari_make(id, scalarize=False, **kwargs): # else: # scalarize = True - scalarize = True #TODO : to remove with th PR :[WIP] Atari part2 (https://github.com/rlberry-py/rlberry/pull/285) + scalarize = True # TODO : to remove with th PR :[WIP] Atari part2 (https://github.com/rlberry-py/rlberry/pull/285) if "atari_wrappers_dict" in kwargs.keys(): atari_wrappers_dict = kwargs.pop("atari_wrappers_dict") From 0cbd97443726b1166820418098c2b3aa71b63fd3 Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 12 Apr 2023 11:08:39 +0200 Subject: [PATCH 63/65] add docstring for atari_make --- rlberry/envs/gym_make.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index b01ecfd6e..28cb478e3 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -34,6 +34,29 @@ def gym_make(id, wrap_spaces=False, **kwargs): def atari_make(id, scalarize=False, **kwargs): + """ + Adaptator to work with 'make_atari_env' in stableBaselines. + WARNING "work in progress" : For the moment, it can't handle VecEnv, it uses only 1 env. (scalarize is forced to True) + (TODO PR : https://github.com/rlberry-py/rlberry/pull/285) + + Parameters + ---------- + id : str + Environment id. + scalarize : bool, default = False + If true, add a wrapper for stable_baselines VecEnv, so that they accept non-vectorized actions, + and return non-vectorized states. (use only the first env from VecEnv) + **kwargs + Optional arguments to configure the environment. + + Examples + -------- + >>> from rlberry.envs.gym_make import atari_make + >>> env_ctor = atari_make + >>> env_kwargs = {"id": "ALE/Freeway-v5", "n_envs":1, "atari_wrappers_dict":dict(terminal_on_life_loss=False)} + >>> env = env_ctor(**env_kwargs) + """ + from stable_baselines3.common.env_util import make_atari_env from stable_baselines3.common.vec_env import VecFrameStack From 5439abf1b97db8adbd46f371d532938e9c6937f3 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 12 Apr 2023 09:11:20 +0000 Subject: [PATCH 64/65] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- rlberry/envs/gym_make.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rlberry/envs/gym_make.py b/rlberry/envs/gym_make.py index f11337f97..a953f373f 100644 --- a/rlberry/envs/gym_make.py +++ b/rlberry/envs/gym_make.py @@ -56,7 +56,7 @@ def atari_make(id, scalarize=False, **kwargs): >>> env_kwargs = {"id": "ALE/Freeway-v5", "n_envs":1, "atari_wrappers_dict":dict(terminal_on_life_loss=False)} >>> env = env_ctor(**env_kwargs) """ - + from stable_baselines3.common.env_util import make_atari_env from stable_baselines3.common.vec_env import VecFrameStack From ca981ac575ddfde6a4ae3ced0700692d0049552a Mon Sep 17 00:00:00 2001 From: JulienT01 Date: Wed, 12 Apr 2023 11:19:52 +0200 Subject: [PATCH 65/65] update changelog --- docs/changelog.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/changelog.rst b/docs/changelog.rst index 23b6d1660..2f549454f 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -9,6 +9,10 @@ Dev version * Move old scripts (jax agents, attention networks, old examples...) that we won't maintain from the main branch to an archive branch. +*PR #277* + +* Add and update code to use "Atari games" env + Version 0.4.0 (latest stable version) --------------------------------------