gryf/coach
1
0
Fork 0
mirror of https://github.com/gryf/coach.git synced 2025-12-17 19:20:19 +01:00
Code Issues Projects Releases Wiki Activity

pre-release 0.10.0

Browse Source
This commit is contained in:
Gal Novik
2018-08-13 17:11:34 +03:00
parent d44c329bb8
commit 19ca5c24b1
485 changed files with 33292 additions and 16770 deletions
Download Patch File Download Diff File Expand all files Collapse all files

144
rl_coach/architectures/tensorflow_components/heads/ppo_head.py Normal file
View File

@@ -0,0 +1,144 @@
#
# Copyright (c) 2017 Intel Corporation
#
# 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
#
# 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
# limitations under the License.
#
import numpy as np
import tensorflow as tf
from rl_coach.base_parameters import AgentParameters
from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace
from rl_coach.spaces import SpacesDefinition
from rl_coach.utils import eps
from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters, normalized_columns_initializer
from rl_coach.core_types import ActionProbabilities
class PPOHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params'):
super().__init__(parameterized_class=PPOHead, activation_function=activation_function, name=name)
class PPOHead(Head):
def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh'):
super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function)
self.name = 'ppo_head'
self.return_type = ActionProbabilities
# used in regular PPO
self.use_kl_regularization = agent_parameters.algorithm.use_kl_regularization
if self.use_kl_regularization:
# kl coefficient and its corresponding assignment operation and placeholder
self.kl_coefficient = tf.Variable(agent_parameters.algorithm.initial_kl_coefficient,
trainable=False, name='kl_coefficient')
self.kl_coefficient_ph = tf.placeholder('float', name='kl_coefficient_ph')
self.assign_kl_coefficient = tf.assign(self.kl_coefficient, self.kl_coefficient_ph)
self.kl_cutoff = 2 * agent_parameters.algorithm.target_kl_divergence
self.high_kl_penalty_coefficient = agent_parameters.algorithm.high_kl_penalty_coefficient
self.clip_likelihood_ratio_using_epsilon = agent_parameters.algorithm.clip_likelihood_ratio_using_epsilon
self.beta = agent_parameters.algorithm.beta_entropy
def _build_module(self, input_layer):
if isinstance(self.spaces.action, DiscreteActionSpace):
self._build_discrete_net(input_layer, self.spaces.action)
elif isinstance(self.spaces.action, BoxActionSpace):
self._build_continuous_net(input_layer, self.spaces.action)
else:
raise ValueError("only discrete or continuous action spaces are supported for PPO")
self.action_probs_wrt_policy = self.policy_distribution.log_prob(self.actions)
self.action_probs_wrt_old_policy = self.old_policy_distribution.log_prob(self.actions)
self.entropy = tf.reduce_mean(self.policy_distribution.entropy())
# Used by regular PPO only
# add kl divergence regularization
self.kl_divergence = tf.reduce_mean(tf.distributions.kl_divergence(self.old_policy_distribution, self.policy_distribution))
if self.use_kl_regularization:
# no clipping => use kl regularization
self.weighted_kl_divergence = tf.multiply(self.kl_coefficient, self.kl_divergence)
self.regularizations = self.weighted_kl_divergence + self.high_kl_penalty_coefficient * \
tf.square(tf.maximum(0.0, self.kl_divergence - self.kl_cutoff))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, self.regularizations)
# calculate surrogate loss
self.advantages = tf.placeholder(tf.float32, [None], name="advantages")
self.target = self.advantages
# action_probs_wrt_old_policy != 0 because it is e^...
self.likelihood_ratio = tf.exp(self.action_probs_wrt_policy - self.action_probs_wrt_old_policy)
if self.clip_likelihood_ratio_using_epsilon is not None:
self.clip_param_rescaler = tf.placeholder(tf.float32, ())
self.input.append(self.clip_param_rescaler)
max_value = 1 + self.clip_likelihood_ratio_using_epsilon * self.clip_param_rescaler
min_value = 1 - self.clip_likelihood_ratio_using_epsilon * self.clip_param_rescaler
self.clipped_likelihood_ratio = tf.clip_by_value(self.likelihood_ratio, min_value, max_value)
self.scaled_advantages = tf.minimum(self.likelihood_ratio * self.advantages,
self.clipped_likelihood_ratio * self.advantages)
else:
self.scaled_advantages = self.likelihood_ratio * self.advantages
# minus sign is in order to set an objective to minimize (we actually strive for maximizing the surrogate loss)
self.surrogate_loss = -tf.reduce_mean(self.scaled_advantages)
if self.is_local:
# add entropy regularization
if self.beta:
self.entropy = tf.reduce_mean(self.policy_distribution.entropy())
self.regularizations = -tf.multiply(self.beta, self.entropy, name='entropy_regularization')
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, self.regularizations)
self.loss = self.surrogate_loss
tf.losses.add_loss(self.loss)
def _build_discrete_net(self, input_layer, action_space):
num_actions = len(action_space.actions)
self.actions = tf.placeholder(tf.int32, [None], name="actions")
self.old_policy_mean = tf.placeholder(tf.float32, [None, num_actions], "old_policy_mean")
self.old_policy_std = tf.placeholder(tf.float32, [None, num_actions], "old_policy_std")
# Policy Head
self.input = [self.actions, self.old_policy_mean]
policy_values = tf.layers.dense(input_layer, num_actions, name='policy_fc')
self.policy_mean = tf.nn.softmax(policy_values, name="policy")
# define the distributions for the policy and the old policy
self.policy_distribution = tf.contrib.distributions.Categorical(probs=self.policy_mean)
self.old_policy_distribution = tf.contrib.distributions.Categorical(probs=self.old_policy_mean)
self.output = self.policy_mean
def _build_continuous_net(self, input_layer, action_space):
num_actions = action_space.shape[0]
self.actions = tf.placeholder(tf.float32, [None, num_actions], name="actions")
self.old_policy_mean = tf.placeholder(tf.float32, [None, num_actions], "old_policy_mean")
self.old_policy_std = tf.placeholder(tf.float32, [None, num_actions], "old_policy_std")
self.input = [self.actions, self.old_policy_mean, self.old_policy_std]
self.policy_mean = tf.layers.dense(input_layer, num_actions, name='policy_mean',
kernel_initializer=normalized_columns_initializer(0.01))
if self.is_local:
self.policy_logstd = tf.Variable(np.zeros((1, num_actions)), dtype='float32',
collections=[tf.GraphKeys.LOCAL_VARIABLES])
else:
self.policy_logstd = tf.Variable(np.zeros((1, num_actions)), dtype='float32')
self.policy_std = tf.tile(tf.exp(self.policy_logstd), [tf.shape(input_layer)[0], 1], name='policy_std')
# define the distributions for the policy and the old policy
self.policy_distribution = tf.contrib.distributions.MultivariateNormalDiag(self.policy_mean, self.policy_std + eps)
self.old_policy_distribution = tf.contrib.distributions.MultivariateNormalDiag(self.old_policy_mean, self.old_policy_std + eps)
self.output = [self.policy_mean, self.policy_std]