research/pcl_rl/baseline.py
# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""Baseline model for value estimates.
Implements the value component of the neural network.
In some cases this is just an additional linear layer on the policy.
In other cases, it is a completely separate neural network.
"""
from six.moves import xrange
import tensorflow as tf
import numpy as np
class Baseline(object):
def __init__(self, env_spec, internal_policy_dim,
input_prev_actions=True,
input_time_step=False,
input_policy_state=True,
n_hidden_layers=0,
hidden_dim=64,
tau=0.0):
self.env_spec = env_spec
self.internal_policy_dim = internal_policy_dim
self.input_prev_actions = input_prev_actions
self.input_time_step = input_time_step
self.input_policy_state = input_policy_state
self.n_hidden_layers = n_hidden_layers
self.hidden_dim = hidden_dim
self.tau = tau
self.matrix_init = tf.truncated_normal_initializer(stddev=0.01)
def get_inputs(self, time_step, obs, prev_actions,
internal_policy_states):
"""Get inputs to network as single tensor."""
inputs = [tf.ones_like(time_step)]
input_dim = 1
if not self.input_policy_state:
for i, (obs_dim, obs_type) in enumerate(self.env_spec.obs_dims_and_types):
if self.env_spec.is_discrete(obs_type):
inputs.append(
tf.one_hot(obs[i], obs_dim))
input_dim += obs_dim
elif self.env_spec.is_box(obs_type):
cur_obs = obs[i]
inputs.append(cur_obs)
inputs.append(cur_obs ** 2)
input_dim += obs_dim * 2
else:
assert False
if self.input_prev_actions:
for i, (act_dim, act_type) in enumerate(self.env_spec.act_dims_and_types):
if self.env_spec.is_discrete(act_type):
inputs.append(
tf.one_hot(prev_actions[i], act_dim))
input_dim += act_dim
elif self.env_spec.is_box(act_type):
inputs.append(prev_actions[i])
input_dim += act_dim
else:
assert False
if self.input_policy_state:
inputs.append(internal_policy_states)
input_dim += self.internal_policy_dim
if self.input_time_step:
scaled_time = 0.01 * time_step
inputs.extend([scaled_time, scaled_time ** 2, scaled_time ** 3])
input_dim += 3
return input_dim, tf.concat(inputs, 1)
def reshape_batched_inputs(self, all_obs, all_actions,
internal_policy_states, policy_logits):
"""Reshape inputs from [time_length, batch_size, ...] to
[time_length * batch_size, ...].
This allows for computing the value estimate in one go.
"""
batch_size = tf.shape(all_obs[0])[1]
time_length = tf.shape(all_obs[0])[0]
reshaped_obs = []
for obs, (obs_dim, obs_type) in zip(all_obs, self.env_spec.obs_dims_and_types):
if self.env_spec.is_discrete(obs_type):
reshaped_obs.append(tf.reshape(obs, [time_length * batch_size]))
elif self.env_spec.is_box(obs_type):
reshaped_obs.append(tf.reshape(obs, [time_length * batch_size, obs_dim]))
reshaped_prev_act = []
reshaped_policy_logits = []
for i, (act_dim, act_type) in enumerate(self.env_spec.act_dims_and_types):
prev_act = all_actions[i]
if self.env_spec.is_discrete(act_type):
reshaped_prev_act.append(
tf.reshape(prev_act, [time_length * batch_size]))
elif self.env_spec.is_box(act_type):
reshaped_prev_act.append(
tf.reshape(prev_act, [time_length * batch_size, act_dim]))
reshaped_policy_logits.append(
tf.reshape(policy_logits[i], [time_length * batch_size, -1]))
reshaped_internal_policy_states = tf.reshape(
internal_policy_states,
[time_length * batch_size, self.internal_policy_dim])
time_step = (float(self.input_time_step) *
tf.expand_dims(
tf.to_float(tf.range(time_length * batch_size) /
batch_size), -1))
return (time_step, reshaped_obs, reshaped_prev_act,
reshaped_internal_policy_states,
reshaped_policy_logits)
def get_values(self, all_obs, all_actions, internal_policy_states,
policy_logits):
"""Get value estimates given input."""
batch_size = tf.shape(all_obs[0])[1]
time_length = tf.shape(all_obs[0])[0]
(time_step, reshaped_obs, reshaped_prev_act,
reshaped_internal_policy_states,
reshaped_policy_logits) = self.reshape_batched_inputs(
all_obs, all_actions, internal_policy_states, policy_logits)
input_dim, inputs = self.get_inputs(
time_step, reshaped_obs, reshaped_prev_act,
reshaped_internal_policy_states)
for depth in xrange(self.n_hidden_layers):
with tf.variable_scope('value_layer%d' % depth):
w = tf.get_variable('w', [input_dim, self.hidden_dim])
inputs = tf.nn.tanh(tf.matmul(inputs, w))
input_dim = self.hidden_dim
w_v = tf.get_variable('w_v', [input_dim, 1],
initializer=self.matrix_init)
values = tf.matmul(inputs, w_v)
values = tf.reshape(values, [time_length, batch_size])
inputs = inputs[:-batch_size] # remove "final vals"
return values, inputs, w_v
class UnifiedBaseline(Baseline):
"""Baseline for Unified PCL."""
def get_values(self, all_obs, all_actions, internal_policy_states,
policy_logits):
batch_size = tf.shape(all_obs[0])[1]
time_length = tf.shape(all_obs[0])[0]
(time_step, reshaped_obs, reshaped_prev_act,
reshaped_internal_policy_states,
reshaped_policy_logits) = self.reshape_batched_inputs(
all_obs, all_actions, internal_policy_states, policy_logits)
def f_transform(q_values, tau):
max_q = tf.reduce_max(q_values, -1, keep_dims=True)
return tf.squeeze(max_q, [-1]) + tau * tf.log(
tf.reduce_sum(tf.exp((q_values - max_q) / tau), -1))
assert len(reshaped_policy_logits) == 1
values = f_transform((self.tau + self.eps_lambda) * reshaped_policy_logits[0],
(self.tau + self.eps_lambda))
values = tf.reshape(values, [time_length, batch_size])
# not used
input_dim, inputs = self.get_inputs(
time_step, reshaped_obs, reshaped_prev_act,
reshaped_internal_policy_states)
w_v = tf.get_variable('w_v', [input_dim, 1],
initializer=self.matrix_init)
inputs = inputs[:-batch_size] # remove "final vals"
return values, inputs, w_v