Env_step.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "597ef362-a10d-4b07-ae4b-6b2fe5f2827c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "import functools\n",
    "from IPython.display import HTML\n",
    "import jax\n",
    "from jax import numpy as jp\n",
    "import numpy as np\n",
    "from typing import Any, Dict, Sequence, Tuple, Union, Optional\n",
    "import wandb\n",
    "\n",
    "from brax import base\n",
    "from brax import envs\n",
    "from brax import math\n",
    "from brax.base import Base, Motion, Transform\n",
    "from brax.envs.base import Env, MjxEnv, State\n",
    "from brax.mjx.base import State as MjxState\n",
    "from brax.training.agents.ppo import train as ppo\n",
    "from brax.training.agents.ppo import networks as ppo_networks\n",
    "from brax.io import html, mjcf, model\n",
    "\n",
    "from etils import epath\n",
    "from flax import struct\n",
    "from matplotlib import pyplot as plt\n",
    "import mediapy as media\n",
    "from ml_collections import config_dict\n",
    "import mujoco\n",
    "from mujoco import mjx\n",
    "import os\n",
    "\n",
    "import yaml\n",
    "from typing import List, Dict, Text\n",
    "\n",
    "# ## TODO:\n",
    "# \n",
    "# - Check the healthy z-range of the rodent. Now the training\n",
    "#     - Check mj_data and how to pull out kinematics of the simulations\n",
    "# - Check the `brax.envs` and how I can pass the custom parameters\n",
    "\n",
    "# In[3]:\n",
    "\n",
    "\n",
    "def load_params(param_path: Text) -> Dict:\n",
    "    with open(param_path, \"rb\") as file:\n",
    "        params = yaml.safe_load(file)\n",
    "    return params\n",
    "\n",
    "\n",
    "params = load_params(\"params/params.yaml\")\n",
    "\n",
    "class Rodent(MjxEnv):\n",
    "    \n",
    "    # Might want to change the terminate_when_unhealthy params to enables\n",
    "    # longer episode length, since the average episode length is too short (1 timestep)\n",
    "    # temp change the `terminate_when_unhealthy` to extend the episode length.\n",
    "    def __init__(\n",
    "            self,\n",
    "            forward_reward_weight=5,\n",
    "            ctrl_cost_weight=0.1,\n",
    "            healthy_reward=0.5,\n",
    "            terminate_when_unhealthy=False,\n",
    "            healthy_z_range=(0.2, 1.0),\n",
    "            reset_noise_scale=1e-2,\n",
    "            exclude_current_positions_from_observation=False,\n",
    "            **kwargs,\n",
    "    ):\n",
    "        params = load_params(\"params/params.yaml\")\n",
    "        mj_model = mujoco.MjModel.from_xml_path(params[\"XML_PATH\"])\n",
    "        mj_model.opt.solver = mujoco.mjtSolver.mjSOL_CG\n",
    "        mj_model.opt.iterations = 6\n",
    "        mj_model.opt.ls_iterations = 6\n",
    "\n",
    "        physics_steps_per_control_step = 5\n",
    "        kwargs['n_frames'] = kwargs.get(\n",
    "            'n_frames', physics_steps_per_control_step)\n",
    "\n",
    "        super().__init__(model=mj_model, **kwargs)\n",
    "\n",
    "        self._forward_reward_weight = forward_reward_weight\n",
    "        self._ctrl_cost_weight = ctrl_cost_weight\n",
    "        self._healthy_reward = healthy_reward\n",
    "        self._terminate_when_unhealthy = terminate_when_unhealthy\n",
    "        self._healthy_z_range = healthy_z_range\n",
    "        self._reset_noise_scale = reset_noise_scale\n",
    "        self._exclude_current_positions_from_observation = (\n",
    "            exclude_current_positions_from_observation\n",
    "        )\n",
    "\n",
    "    def reset(self, rng: jp.ndarray) -> State:\n",
    "        \"\"\"Resets the environment to an initial state.\"\"\"\n",
    "        rng, rng1, rng2 = jax.random.split(rng, 3)\n",
    "\n",
    "        low, hi = -self._reset_noise_scale, self._reset_noise_scale\n",
    "        qpos = self.sys.qpos0 + jax.random.uniform(\n",
    "            rng1, (self.sys.nq,), minval=low, maxval=hi\n",
    "        )\n",
    "        qvel = jax.random.uniform(\n",
    "            rng2, (self.sys.nv,), minval=low, maxval=hi\n",
    "        )\n",
    "\n",
    "        data = self.pipeline_init(qpos, qvel)\n",
    "\n",
    "        obs = self._get_obs(data.data, jp.zeros(self.sys.nu))\n",
    "        reward, done, zero = jp.zeros(3)\n",
    "        metrics = {\n",
    "            'forward_reward': zero,\n",
    "            'reward_linvel': zero,\n",
    "            'reward_quadctrl': zero,\n",
    "            'reward_alive': zero,\n",
    "            'x_position': zero,\n",
    "            'y_position': zero,\n",
    "            'distance_from_origin': zero,\n",
    "            'x_velocity': zero,\n",
    "            'y_velocity': zero,\n",
    "        }\n",
    "        return State(data, obs, reward, done, metrics)\n",
    "\n",
    "    def step(self, state: State, action: jp.ndarray) -> State:\n",
    "        \"\"\"Runs one timestep of the environment's dynamics.\"\"\"\n",
    "        data0 = state.pipeline_state\n",
    "        data = self.pipeline_step(data0, action)\n",
    "        # based on the timestep simulation, calculate the rewards\n",
    "        com_before = data0.data.subtree_com[1]\n",
    "        com_after = data.data.subtree_com[1]\n",
    "        velocity = (com_after - com_before) / self.dt\n",
    "        forward_reward = self._forward_reward_weight * velocity[0]\n",
    "\n",
    "        min_z, max_z = self._healthy_z_range\n",
    "        is_healthy = jp.where(data.q[2] < min_z, 0.0, 1.0)\n",
    "        is_healthy = jp.where(data.q[2] > max_z, 0.0, is_healthy)\n",
    "        if self._terminate_when_unhealthy:\n",
    "            healthy_reward = self._healthy_reward\n",
    "        else:\n",
    "            healthy_reward = self._healthy_reward * is_healthy\n",
    "\n",
    "        ctrl_cost = self._ctrl_cost_weight * jp.sum(jp.square(action))\n",
    "\n",
    "        obs = self._get_obs(data.data, action)\n",
    "        reward = forward_reward + healthy_reward - ctrl_cost\n",
    "        # terminates when unhealthy\n",
    "        done = 1.0 - is_healthy if self._terminate_when_unhealthy else 0.0\n",
    "        state.metrics.update(\n",
    "            forward_reward=forward_reward,\n",
    "            reward_linvel=forward_reward,\n",
    "            reward_quadctrl=-ctrl_cost,\n",
    "            reward_alive=healthy_reward,\n",
    "            x_position=com_after[0],\n",
    "            y_position=com_after[1],\n",
    "            distance_from_origin=jp.linalg.norm(com_after),\n",
    "            x_velocity=velocity[0],\n",
    "            y_velocity=velocity[1],\n",
    "        )\n",
    "\n",
    "        return state.replace(\n",
    "            pipeline_state=data, obs=obs, reward=reward, done=done\n",
    "        )\n",
    "\n",
    "    def _get_obs(\n",
    "            self, data: mjx.Data, action: jp.ndarray\n",
    "    ) -> jp.ndarray:\n",
    "        \"\"\"Observes humanoid body position, velocities, and angles.\"\"\"\n",
    "        position = data.qpos\n",
    "        if self._exclude_current_positions_from_observation:\n",
    "            position = position[2:]\n",
    "            \n",
    "        # external_contact_forces are excluded\n",
    "        return jp.concatenate([\n",
    "            position,\n",
    "            data.qvel,\n",
    "            data.cinert[1:].ravel(),\n",
    "            data.cvel[1:].ravel(),\n",
    "            data.qfrc_actuator,\n",
    "        ])\n",
    "\n",
    "\n",
    "# ## training loop\n",
    "\n",
    "# In[5]:\n",
    "\n",
    "\n",
    "# envs.register_environment('rodent', Rodent)\n",
    "\n",
    "# # instantiate the environment\n",
    "# env_name = 'rodent'\n",
    "rodent_env = Rodent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d63c456",
   "metadata": {},
   "outputs": [],
   "source": [
    "ant_env = envs.get_environment(\"ant\")\n",
    "humanoid_env = envs.get_environment(\"humanoid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "edc1f864-693d-4121-960f-7d3998463dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the jit reset/step functions\n",
    "# jit_reset = jax.jit(env.reset)\n",
    "# jit_step = jax.jit(env.step)\n",
    "\n",
    "# initialize the state\n",
    "rng = jax.random.PRNGKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ead2f38-827a-47f0-98c0-a11b006b3b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the state\n",
    "rng = jax.random.PRNGKey(0)\n",
    "rodent_state = rodent_env.reset(rng)\n",
    "ant_state = ant_env.reset(rng)\n",
    "humanoid_state = humanoid_env.reset(rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1c7dd1",
   "metadata": {},
   "source": [
    "## Notes\n",
    "\n",
    "Since the observation size is insanly large for our rodent, this might lead to a memory overflow during training. We might want to rethink which observation should be pased into the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4970d554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rodent, Ant, Humanoid Observation shapes are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1260, 27, 244)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Rodent, Ant, Humanoid Observation shapes are:\")\n",
    "len(rodent_state.obs), len(ant_state.obs), len(humanoid_state.obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c28cbe22",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = rodent_state.pipeline_state.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "727168d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       -5.16515807e-04,  3.97377722e-02, -4.98117320e-03, -2.84828991e-03,\n",
       "       -6.77912235e-02,  1.61476746e-01, -8.91518816e-02,  8.74280185e-02,\n",
       "        3.95370573e-02,  3.55898440e-02,  2.37464905e-04,  5.16527798e-04,\n",
       "       -6.53819069e-02,  1.61252320e-01, -8.90082046e-02,  8.73997137e-02,\n",
       "        3.96424942e-02], dtype=float32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.qfrc_actuator"
   ]
  },
  {
   "cell_type": "code",
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      "0.009299147\n",
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      "-0.008541888\n",
      "-0.007484456\n",
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      "-0.008715402\n",
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      "-0.008724502\n",
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      "0.014620232\n",
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      "0.0059213378\n",
      "-0.008721647\n",
      "-0.007080201\n",
      "-0.0014886148\n",
      "0.01819521\n",
      "0.007885419\n",
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      "-0.008725842\n",
      "-0.006775896\n",
      "-0.0019741622\n",
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      "0.0075924485\n",
      "0.0061092903\n",
      "-0.008491289\n",
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      "-0.011111299\n",
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      "-0.008765501\n",
      "-0.0016731656\n",
      "-0.011787557\n",
      "0.014302681\n",
      "0.0061379704\n",
      "-0.008965525\n",
      "-0.008801836\n",
      "0.0011626073\n",
      "-0.016374506\n",
      "0.01419319\n",
      "0.0059201894\n",
      "-0.009089518\n",
      "-0.009247758\n",
      "0.0037949178\n",
      "-0.021468848\n",
      "0.014079813\n",
      "0.005635833\n",
      "-0.009225423\n",
      "-0.009743184\n",
      "0.0026029751\n",
      "-0.0003973834\n",
      "-0.0010678378\n",
      "0.0060580177\n",
      "-0.008070603\n",
      "-0.008147788\n",
      "0.002600784\n",
      "0.002667871\n",
      "0.003058827\n",
      "0.0060303137\n",
      "-0.008361714\n",
      "-0.007931568\n",
      "0.0027277374\n",
      "-0.0050499234\n",
      "0.0045679645\n",
      "0.0057888124\n",
      "-0.008447558\n",
      "-0.00835026\n",
      "0.0050963266\n",
      "-0.0009496864\n",
      "0.0045871623\n",
      "0.005981071\n",
      "-0.008560454\n",
      "-0.007958618\n",
      "0.008352527\n",
      "0.0058419434\n",
      "0.004619323\n",
      "0.006356154\n",
      "-0.008743372\n",
      "-0.0073065995\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "-0.037147347\n",
      "0.104761586\n",
      "-0.16270038\n",
      "0.06281579\n",
      "0.25441393\n",
      "0.021953363\n",
      "-0.03450281\n",
      "0.10162756\n",
      "-0.15900536\n",
      "0.061401986\n",
      "0.24748002\n",
      "0.02225221\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.017569717\n",
      "-0.0005165158\n",
      "0.039737772\n",
      "-0.004981173\n",
      "-0.00284829\n",
      "-0.06779122\n",
      "0.16147675\n",
      "-0.08915188\n",
      "0.08742802\n",
      "0.039537057\n",
      "0.035589844\n",
      "0.0002374649\n",
      "0.0005165278\n",
      "-0.06538191\n",
      "0.16125232\n",
      "-0.089008205\n",
      "0.08739971\n",
      "0.039642494\n"
     ]
    }
   ],
   "source": [
    "for i in rodent_state.obs:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "860658e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([ 4.0483385e-02, -8.9671416e-03,  7.5515956e-02,  9.9994427e-01,\n",
       "        1.7467347e-03,  4.5650802e-03,  9.3545876e-03,  4.9890876e-03,\n",
       "        1.6337344e-03,  8.5870493e-03,  5.5944296e-03,  9.3859006e-03,\n",
       "        9.7195767e-03,  5.6019062e-03, -1.0457542e-04,  9.3679037e-03,\n",
       "        4.0152110e-05, -7.7162432e-03, -2.7349759e-03, -1.0094289e-03,\n",
       "        7.7305045e-03,  3.2095481e-03,  3.5746768e-03,  9.6185282e-03,\n",
       "       -8.7119024e-03, -3.4158942e-03, -8.7743187e-03, -6.9099306e-03,\n",
       "        4.9248980e-03,  8.8754930e-03,  1.8202513e-04,  3.8369969e-03,\n",
       "       -9.4614746e-03, -1.8994613e-03, -9.1804713e-03,  8.0775768e-03,\n",
       "        2.2244621e-03,  9.9429823e-03, -9.6811503e-03,  6.0554892e-03,\n",
       "       -6.9334744e-03, -2.6698112e-03, -7.7600381e-03,  9.1857575e-03,\n",
       "       -8.7164547e-03,  2.3231469e-04,  2.4697995e-03, -1.8483903e-03,\n",
       "       -9.1607589e-04,  3.8361549e-03,  6.8340991e-03,  9.9727586e-03,\n",
       "       -8.5709523e-04, -7.1314145e-03, -2.9011010e-03,  4.0631173e-03,\n",
       "       -2.3284983e-03, -7.1229767e-03, -9.3572401e-04,  9.9623464e-03,\n",
       "        5.6965798e-03,  7.8045893e-03,  2.7962402e-03, -8.2596922e-03,\n",
       "       -1.6155839e-03, -5.3429673e-03,  7.3601343e-03, -4.7492981e-04,\n",
       "       -1.0330556e-03, -2.2646692e-04,  3.5443567e-03, -8.7386081e-03,\n",
       "       -1.3325643e-03, -7.4517224e-03], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7eb37164",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'State' object has no attribute 'qvel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m [\n\u001b[0;32m----> 2\u001b[0m             \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqvel\u001b[49m,\n\u001b[1;32m      3\u001b[0m             data\u001b[38;5;241m.\u001b[39mcinert[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m      4\u001b[0m             data\u001b[38;5;241m.\u001b[39mcvel[\u001b[38;5;241m1\u001b[39m:]\u001b[38;5;241m.\u001b[39mravel(),\n\u001b[1;32m      5\u001b[0m             data\u001b[38;5;241m.\u001b[39mqfrc_actuator,\n\u001b[1;32m      6\u001b[0m         ]\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'State' object has no attribute 'qvel'"
     ]
    }
   ],
   "source": [
    "[\n",
    "            data.qvel,\n",
    "            data.cinert[1:].ravel(),\n",
    "            data.cvel[1:].ravel(),\n",
    "            data.qfrc_actuator,\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34c4026c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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