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model.py
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import numpy as np
import tensorflow as tf
import tensorflow.contrib.rnn as rnn
from ops import flatten, conv2d, linear
def normalized_columns_initializer(std=1.0):
def _initializer(shape, dtype=None, partition_info=None):
out = np.random.randn(*shape).astype(np.float32)
out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
return tf.constant(out)
return _initializer
def categorical_sample(logits, d):
value = tf.squeeze(tf.multinomial(logits - tf.reduce_max(logits, [1], keep_dims=True), 1), [1])
return tf.one_hot(value, d)
class SubPolicy(object):
def __init__(self, ob_space, ac_space, subgoal_space, intrinsic_type):
self.x = x = tf.placeholder(tf.float32, [None] + list(ob_space), name='x')
self.action_prev = action_prev = tf.placeholder(tf.float32, [None, ac_space], name='action_prev')
self.reward_prev = reward_prev = tf.placeholder(tf.float32, [None, 1], name='reward_prev')
self.subgoal = subgoal = tf.placeholder(tf.float32, [None, subgoal_space], name='subgoal')
self.intrinsic_type = intrinsic_type
with tf.variable_scope('encoder'):
x = tf.image.resize_images(x, [84, 84])
x = x / 255.0
self.p = x
x = tf.nn.relu(conv2d(x, 16, "l1", [8, 8], [4, 4]))
x = tf.nn.relu(conv2d(x, 32, "l2", [4, 4], [2, 2]))
self.f = tf.reduce_mean(x, axis=[1, 2])
x = flatten(x)
with tf.variable_scope('sub_policy'):
x = tf.nn.relu(linear(x, 256, "fc",
normalized_columns_initializer(0.01)))
x = tf.concat([x, action_prev], axis=1)
x = tf.concat([x, reward_prev], axis=1)
x = tf.concat([x, subgoal], axis=1)
# introduce a "fake" batch dimension of 1 after flatten
# so that we can do LSTM over time dim
x = tf.expand_dims(x, [0])
size = 256
lstm = rnn.BasicLSTMCell(size, state_is_tuple=True)
self.state_size = lstm.state_size
step_size = tf.shape(self.x)[:1]
c_init = np.zeros((1, lstm.state_size.c), np.float32)
h_init = np.zeros((1, lstm.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h])
self.state_in = [c_in, h_in]
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm, x, initial_state=state_in, sequence_length=step_size,
time_major=False
)
lstm_c, lstm_h = lstm_state
lstm_outputs = tf.reshape(lstm_outputs, [-1, size])
self.logits = linear(lstm_outputs, ac_space, "action",
normalized_columns_initializer(0.01))
self.vf = tf.reshape(linear(lstm_outputs, 1, "value",
normalized_columns_initializer(1.0)), [-1])
self.state_out = [lstm_c[:1, :], lstm_h[:1, :]]
self.sample = categorical_sample(self.logits, ac_space)[0, :]
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
def get_initial_features(self):
return self.state_init
def act(self, ob, action_prev, reward_prev, subgoal, c, h):
sess = tf.get_default_session()
return sess.run([self.sample, self.vf] + self.state_out,
{self.x: [ob], self.state_in[0]: c, self.state_in[1]: h,
self.action_prev: [action_prev],
self.reward_prev: [reward_prev],
self.subgoal: [subgoal]})
def value(self, ob, action_prev, reward_prev, subgoal, c, h):
sess = tf.get_default_session()
return sess.run(self.vf, {self.x: [ob], self.state_in[0]: c,
self.state_in[1]: h,
self.action_prev: [action_prev],
self.reward_prev: [reward_prev],
self.subgoal: [subgoal]})[0]
def feature(self, state):
sess = tf.get_default_session()
if self.intrinsic_type == 'feature':
return sess.run(self.f, {self.x: [state]})[0, :]
else:
return sess.run(self.p, {self.x: [state]})[0, :]
class MetaPolicy(object):
def __init__(self, ob_space, subgoal_space, intrinsic_type):
self.x = x = \
tf.placeholder(tf.float32, [None] + list(ob_space), name='x_meta')
self.subgoal_prev = subgoal_prev = \
tf.placeholder(tf.float32, [None, subgoal_space], name='subgoal_prev')
self.reward_prev = reward_prev = \
tf.placeholder(tf.float32, [None, 1], name='reward_prev_meta')
self.intrinsic_type = intrinsic_type
with tf.variable_scope('encoder', reuse=True):
x = tf.image.resize_images(x, [84, 84])
x = x / 255.0
x = tf.nn.relu(conv2d(x, 16, "l1", [8, 8], [4, 4]))
x = tf.nn.relu(conv2d(x, 32, "l2", [4, 4], [2, 2]))
x = flatten(x)
with tf.variable_scope('meta_policy'):
x = tf.nn.relu(linear(x, 256, "fc",
normalized_columns_initializer(0.01)))
x = tf.concat([x, subgoal_prev], axis=1)
x = tf.concat([x, reward_prev], axis=1)
# introduce a "fake" batch dimension of 1 after flatten
# so that we can do LSTM over time dim
x = tf.expand_dims(x, [0])
size = 256
lstm = rnn.BasicLSTMCell(size, state_is_tuple=True)
self.state_size = lstm.state_size
step_size = tf.shape(self.x)[:1]
c_init = np.zeros((1, lstm.state_size.c), np.float32)
h_init = np.zeros((1, lstm.state_size.h), np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm.state_size.h])
self.state_in = [c_in, h_in]
state_in = rnn.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
lstm, x, initial_state=state_in, sequence_length=step_size,
time_major=False)
lstm_c, lstm_h = lstm_state
lstm_outputs = tf.reshape(lstm_outputs, [-1, size])
self.logits = linear(lstm_outputs, subgoal_space, "action",
normalized_columns_initializer(0.01))
self.vf = tf.reshape(linear(lstm_outputs, 1, "value",
normalized_columns_initializer(1.0)), [-1])
self.state_out = [lstm_c[:1, :], lstm_h[:1, :]]
self.sample = categorical_sample(self.logits, subgoal_space)[0, :]
self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
def get_initial_features(self):
return self.state_init
def act(self, ob, subgoal_prev, reward_prev, c, h):
sess = tf.get_default_session()
return sess.run([self.sample, self.vf] + self.state_out,
{self.x: [ob], self.state_in[0]: c, self.state_in[1]: h,
self.subgoal_prev: [subgoal_prev],
self.reward_prev: [reward_prev]})
def value(self, ob, subgoal_prev, reward_prev, c, h):
sess = tf.get_default_session()
return sess.run(self.vf, {self.x: [ob], self.state_in[0]: c,
self.state_in[1]: h,
self.subgoal_prev: [subgoal_prev],
self.reward_prev: [reward_prev]})[0]