# # 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.architectures.tensorflow_components.architecture import Dense from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters, normalized_columns_initializer from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace from rl_coach.spaces import SpacesDefinition from rl_coach.utils import eps class PPOHeadParameters(HeadParameters): def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params', dense_layer=Dense): super().__init__(parameterized_class=PPOHead, activation_function=activation_function, name=name, dense_layer=dense_layer) 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', dense_layer=Dense): super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, dense_layer=dense_layer) 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 = self.dense_layer(num_actions)(input_layer, 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 = self.dense_layer(num_actions)(input_layer, 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]