From 6a09ca1dd40bd7036a0d3995e4e32564b98fbb7d Mon Sep 17 00:00:00 2001 From: Guy Koren Date: Wed, 3 Apr 2019 13:07:03 +0300 Subject: [PATCH 01/11] SAC algorithm --- rl_coach/agents/soft_actor_critic_agent.py | 405 ++++++++++++++++++ rl_coach/architectures/head_parameters.py | 13 + .../tensorflow_components/heads/__init__.py | 6 +- .../tensorflow_components/heads/sac_head.py | 137 ++++++ .../tensorflow_components/heads/sac_q_head.py | 126 ++++++ .../tensorflow_components/heads/v_head.py | 8 +- rl_coach/presets/Mujoco_SAC.py | 138 ++++++ 7 files changed, 830 insertions(+), 3 deletions(-) create mode 100644 rl_coach/agents/soft_actor_critic_agent.py create mode 100644 rl_coach/architectures/tensorflow_components/heads/sac_head.py create mode 100644 rl_coach/architectures/tensorflow_components/heads/sac_q_head.py create mode 100644 rl_coach/presets/Mujoco_SAC.py diff --git a/rl_coach/agents/soft_actor_critic_agent.py b/rl_coach/agents/soft_actor_critic_agent.py new file mode 100644 index 000000000..45007b246 --- /dev/null +++ b/rl_coach/agents/soft_actor_critic_agent.py @@ -0,0 +1,405 @@ +# +# 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. +# + +from typing import Union +import copy +import numpy as np +from collections import OrderedDict +import pickle + +from rl_coach.agents.agent import Agent +from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent + +# import heads +from rl_coach.architectures.head_parameters import SACQHeadParameters,SACPolicyHeadParameters,VHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters +from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme +from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase +from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters +# from rl_coach.logger import screen +from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.spaces import BoxActionSpace + + +# There are 3 networks in SAC implementation. All have the same topology but parameters are not shared. +# The networks are: +# 1. State Value Network - SACValueNetwork +# 2. Soft Q Value Network - SACCriticNetwork +# 3. Policy Network - SACPolicyNetwork - currently supporting only Gaussian Policy + + +# 1. State Value Network - SACValueNetwork +# this is the state value network in SAC. +# The network is trained to predict (regression) the state value in the max-entropy settings +# The objective to be minimized is given in equation (5) in the paper: +# +# J(psi)= E_(s~D)[0.5*(V_psi(s)-y(s))^2] +# where y(s) = E_(a~pi)[Q_theta(s,a)-log(pi(a|s))] + + +# Default parameters for value network: +# topology : +# input embedder : EmbedderScheme.Medium (Dense(256)) , relu activation +# middleware : EmbedderScheme.Medium (Dense(256)) , relu activation + + +class SACValueNetworkParameters(NetworkParameters): + def __init__(self): + super().__init__() + self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')} + self.middleware_parameters = FCMiddlewareParameters(activation_function='relu') + self.heads_parameters = [VHeadParameters()] + self.rescale_gradient_from_head_by_factor = [1] + self.optimizer_type = 'Adam' + self.batch_size = 256 + self.async_training = False + self.learning_rate = 0.0003 # 3e-4 see annex D in the paper + self.create_target_network = True # tau is set in SoftActorCriticAlgorithmParameters.rate_for_copying_weights_to_target + + +# 2. Soft Q Value Network - SACCriticNetwork +# the whole network is built in the SACQHeadParameters. we use empty input embedder and middleware +class SACCriticNetworkParameters(NetworkParameters): + def __init__(self): + super().__init__() + self.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty)} + self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty) + self.heads_parameters = [SACQHeadParameters()] # SACQHeadParameters includes the topology of the head + self.rescale_gradient_from_head_by_factor = [1] # todo: its not in the NetworkParameters base. shouldnt it be ? Yes ! + self.optimizer_type = 'Adam' + self.batch_size = 256 + self.async_training = False + self.learning_rate = 0.0003 + self.create_target_network = False + + +# 3. policy Network +# Default parameters for policy network: +# topology : +# input embedder : EmbedderScheme.Medium (Dense(256)) , relu activation +# middleware : EmbedderScheme = [Dense(256)] , relu activation --> scheme should be overridden in preset +class SACPolicyNetworkParameters(NetworkParameters): + def __init__(self): + super().__init__() + self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')} + self.middleware_parameters = FCMiddlewareParameters(activation_function='relu') + self.heads_parameters = [SACPolicyHeadParameters()] + self.rescale_gradient_from_head_by_factor = [1] + self.optimizer_type = 'Adam' + self.batch_size = 256 + self.async_training = False + self.learning_rate = 0.0003 + self.create_target_network = False + self.l2_regularization = 0 # weight decay regularization. not used in the original paper + + +# Algorithm Parameters + +class SoftActorCriticAlgorithmParameters(AlgorithmParameters): + def __init__(self): + super().__init__() + self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1) + self.rate_for_copying_weights_to_target = 0.005 # = Tau as defined in the paper + self.use_deterministic_for_evaluation = True # evaluate agent using deterministic policy (i.e. take the mean value) + + +class SoftActorCriticAgentParameters(AgentParameters): + def __init__(self): + super().__init__(algorithm=SoftActorCriticAlgorithmParameters(), + exploration=AdditiveNoiseParameters(), # todo in SAC: should be re-examined + memory=ExperienceReplayParameters(), # SAC doesnt use episodic related data + # network wrappers: + networks=OrderedDict([("policy", SACPolicyNetworkParameters()), + ("q", SACCriticNetworkParameters()), + ("v", SACValueNetworkParameters())])) + + @property + def path(self): + return 'rl_coach.agents.soft_actor_critic_agent:SoftActorCriticAgent' + # return 'rl_coach.contrib.sac.soft_actor_critic_agent:SoftActorCriticAgent' + +# Soft Actor Critic - https://arxiv.org/abs/1801.01290 +class SoftActorCriticAgent(PolicyOptimizationAgent): + def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None): + super().__init__(agent_parameters, parent) + self.last_gradient_update_step_idx = 0 + + # register signals to track (in learn_from_batch) + self.policy_means = self.register_signal('Policy_mu_avg') + self.policy_logsig = self.register_signal('Policy_logsig') + self.policy_logprob_sampled = self.register_signal('Policy_logp_sampled') + self.policy_grads = self.register_signal('Policy_grads_sumabs') + + self.q1_values = self.register_signal("Q1") + self.TD_err1 = self.register_signal("TD err1") + self.q2_values = self.register_signal("Q2") + self.TD_err2 = self.register_signal("TD err2") + self.v_tgt_ns = self.register_signal('V_tgt_ns') + self.v_onl_ys = self.register_signal('V_onl_ys') + + + + self.action_signal = self.register_signal("actions") + + def dump_state(self,batch,sampled_raw_actions,sampled_actions): + + # State Value Network + value_network = self.networks['v'] + + # Critic Network + q_network = self.networks['q'].online_network + + # Actor (policy) Network + policy_network = self.networks['policy'].online_network + policy_network_keys = self.ap.network_wrappers['policy'].input_embedders_parameters.keys() + + + # dump batch observations + with open('batch_obs.pkl', 'wb') as f: + pickle.dump(batch.states(policy_network_keys)['observation'], f, pickle.HIGHEST_PROTOCOL) + + # dump sampled actions and raw actions + with open('sampled_actions.pkl', 'wb') as f: + pickle.dump(sampled_raw_actions, f, pickle.HIGHEST_PROTOCOL) + pickle.dump(sampled_actions, f, pickle.HIGHEST_PROTOCOL) + + # dump policy network parameters + polnet = [policy_network.weights[l].eval(policy_network.sess) for l in range(len(policy_network.weights))] + with open('c_policy.pkl', 'wb') as f: + pickle.dump(polnet, f, pickle.HIGHEST_PROTOCOL) + + + + vnet_onl = [value_network.online_network.weights[l].eval(value_network.sess) for l in + range(len(value_network.online_network.weights))] + vnet_tgt = [value_network.target_network.weights[l].eval(value_network.sess) for l in + range(len(value_network.target_network.weights))] + with open('c_v_tgt.pkl', 'wb') as f: + pickle.dump(vnet_tgt, f, pickle.HIGHEST_PROTOCOL) + + with open('c_v_onl.pkl', 'wb') as f: + pickle.dump(vnet_onl, f, pickle.HIGHEST_PROTOCOL) + + qnet = [q_network.weights[l].eval(q_network.sess) for l in range(len(q_network.weights))] + with open('c_q.pkl', 'wb') as f: + pickle.dump(qnet, f, pickle.HIGHEST_PROTOCOL) + + return + + def learn_from_batch(self, batch): + ######################################### + # need to update the following networks: + # 1. actor (policy) + # 2. state value (v) + # 3. critic (q1 and q2) + # 4. target network - probably already handled by V + + ######################################### + # define the networks to be used + + # State Value Network + value_network = self.networks['v'] + value_network_keys = self.ap.network_wrappers['v'].input_embedders_parameters.keys() + + # Critic Network + q_network = self.networks['q'].online_network + q_head = q_network.output_heads[0] + q_network_keys = self.ap.network_wrappers['q'].input_embedders_parameters.keys() + + # Actor (policy) Network + policy_network = self.networks['policy'].online_network + policy_head = policy_network.output_heads[0] + policy_network_keys = self.ap.network_wrappers['policy'].input_embedders_parameters.keys() + + ########################################## + # 1. updating the actor - according to (13) in the paper + policy_inputs = copy.copy(batch.states(policy_network_keys)) + policy_results = policy_network.predict(policy_inputs, + outputs=[policy_head.raw_actions,policy_head.actions]) + + sampled_raw_actions,sampled_actions = policy_results + + + policy_mu,policy_std = policy_network.predict(policy_inputs, + outputs=[policy_head.policy_mean,policy_head.policy_log_std]) + + self.policy_means.add_sample(policy_mu) + self.policy_logsig.add_sample(policy_std) + # self.action_signal.add_sample(sampled_actions) + + # now feed the sampled raw actions to get their log probability + policy_inputs['output_0_0'] = sampled_raw_actions + sampled_actions_logprob = policy_network.predict(policy_inputs, + outputs=[policy_head.given_action_logprob]) + self.policy_logprob_sampled.add_sample(sampled_actions_logprob) + + # get the state-action values for the replayed states and their corresponding actions from the policy + # should we do parallel prediction here ? what does it give us ? + q_inputs = copy.copy(batch.states(q_network_keys)) + q_inputs['output_0_0'] = sampled_actions + log_target = q_network.predict(q_inputs).squeeze() + + # log internal q values + q1_vals,q2_vals = q_network.predict(q_inputs,outputs=[q_head.q1_output,q_head.q2_output]) + self.q1_values.add_sample(q1_vals) + self.q2_values.add_sample(q2_vals) + + + + # calculate the gradients according to (13) + # to get grads (unweighted) we need to feed weights of ones and ask for the weighted gradients + # log prob of the given action is index 4 in the sac head's outputs list + + initial_feed_dict = {policy_network.gradients_weights_ph[5]:np.array(1.0)} + # get the gradients of log_prob w.r.t the weights (parameters) - indicated as phi in the paper + dlogp_dphi = policy_network.predict(policy_inputs, + outputs=policy_network.weighted_gradients[5], + initial_feed_dict=initial_feed_dict) + + # the inputs that are defined in the policy head are added to the networks input's dictionary with + # key 'output_0_i' where i is the index of the input. + # in this case, we have given_actions as placeholder and its saved with key output_0_0 + dlogp_dra = policy_network.predict(policy_inputs, + outputs=policy_network.gradients_wrt_inputs[4]['output_0_0']) + dlogp_dra_normalized = np.divide(dlogp_dra,batch.size) + + # dra_dphi is the gradient of the sampled raw actions w.r.t the weights + initial_feed_dict = {policy_network.gradients_weights_ph[2]: dlogp_dra_normalized} + dra_dphi = policy_network.predict(policy_inputs, + outputs=policy_network.weighted_gradients[2], + initial_feed_dict=initial_feed_dict) + + # calculate dq_da + dq_da = q_network.predict(q_inputs, + outputs=q_network.gradients_wrt_inputs[0]['output_0_0']) + dq_da_normalized = np.divide(dq_da,batch.size) + + # calculate da_dphi + initial_feed_dict = {policy_network.gradients_weights_ph[3]: dq_da_normalized} + da_dphi = policy_network.predict(policy_inputs, + outputs=policy_network.weighted_gradients[3], + initial_feed_dict=initial_feed_dict) + + # now given dlogp_dphi, dra_dphi and da_dphi we need to calculate the policy gradients according to (13) + # policy_grads = [(grads_wrt_weights[layer],weights[layer]) for layer in policy_network] + # where: grads_wrt_weights[layer] = dlogp_dphi[layer]+dra_dphi[layer]-da_dphi[layer] + policy_grads = [dlogp_dphi[l]+dra_dphi[l]-da_dphi[l] for l in range(len(dlogp_dphi))] + + # apply the gradients to policy networks + policy_network.apply_gradients(policy_grads) + grads_sumabs=np.sum([np.sum(np.abs(policy_grads[l])) for l in range(len(policy_grads))]) + self.policy_grads.add_sample(grads_sumabs) + ########################################## + # 2. updating the state value online network weights + # done by calculating the targets for the v head according to (5) in the paper + # value_targets = log_targets-sampled_actions_logprob + value_inputs = copy.copy(batch.states(value_network_keys)) + value_targets = log_target - sampled_actions_logprob + + self.v_onl_ys.add_sample(value_targets) + + + # call value_network apply gradients with this target + value_loss = value_network.online_network.train_on_batch(value_inputs,value_targets[:,None])[0] + # todo: what do I do with the value_loss ? + + # v_tgt_state = value_network.target_network.predict(value_inputs) # need v_tgt_state for debug + # self.v_tgt_s.add_sample(v_tgt_state) + + ########################################## + # 3. updating the critic (q networks) + # updating q networks according to (7) in the paper + + # define the input to the q network: state has been already updated previously, but now we need + # the actions from the batch (and not those sampled by the policy) + q_inputs['output_0_0'] = batch.actions(len(batch.actions().shape) == 1) + + # define the targets : scale_reward * reward + (1-terminal)*discount*v_target_next_state + # define v_target_next_state + value_inputs = copy.copy(batch.next_states(value_network_keys)) + v_target_next_state = value_network.target_network.predict(value_inputs) + self.v_tgt_ns.add_sample(v_target_next_state) + # Note: reward is assumed to be rescaled by RewardRescaleFilter in the preset parameters + TD_targets = batch.rewards(expand_dims=True) + \ + (1.0 - batch.game_overs(expand_dims=True)) * self.ap.algorithm.discount * v_target_next_state + + # call critic network update + result = q_network.train_on_batch(q_inputs,TD_targets,additional_fetches=[q_head.q1_loss,q_head.q2_loss]) + total_loss, losses, unclipped_grads = result[:3] + q1_loss,q2_loss = result[3] + self.TD_err1.add_sample(q1_loss) + self.TD_err2.add_sample(q2_loss) + + + ########################################## + # 4. updating the value target network + # I just need to set the parameter rate_for_copying_weights_to_target in the agent parameters to be 1-tau + # where tau is the hyper parameter as defined in sac original implementation + + return total_loss, losses, unclipped_grads + + def get_prediction(self, states): + ''' + get the mean and stdev of the policy distribution given 'states' + :param states: the states for which we need to sample actions from the policy + :return: mean and stdev ? what about logprob ? + ''' + tf_input_state = self.prepare_batch_for_inference(states, 'policy') + return self.networks['policy'].online_network.predict(tf_input_state) + + + def train(self): + # since the algorithm works with experience replay buffer (non-episodic), + # we cant use the policy optimization train method. we need Agent.train + # note that since in Agent.train there is no apply_gradients, we need to do it in learn from batch + return Agent.train(self) + + def choose_action(self, curr_state): + ''' + choose_action - chooses the most likely action + if 'deterministic' - take the mean of the policy which is the prediction of the policy network. + else - use the exploration policy + :param curr_state: + :return: action wrapped in ActionInfo + ''' + + if not isinstance(self.spaces.action, BoxActionSpace): + raise ValueError("SAC works only for continuous control problems") + # convert to batch so we can run it through the network + tf_input_state = self.prepare_batch_for_inference(curr_state, 'policy') + # use the online network for prediction + policy_network = self.networks['policy'].online_network + policy_head = policy_network.output_heads[0] + result = policy_network.predict(tf_input_state, + outputs=[policy_head.policy_mean,policy_head.actions]) + action_mean,action_sample = result + + # if using deterministic policy, take the mean values. else, use exploration policy to sample from the pdf + # todo in sac : use the exploration policy to distinguish between training and evaluation. + if self.phase == RunPhase.TEST and self.ap.algorithm.use_deterministic_for_evaluation: + action = action_mean[0] + else: + action = action_sample[0] + + # todo in SAC: delete the below. kept for reference during debug + # action_values = actor_network.predict(tf_input_state).squeeze() + # action = self.exploration_policy.get_action(action_values) + + self.action_signal.add_sample(action) + + action_info = ActionInfo(action=action) + return action_info diff --git a/rl_coach/architectures/head_parameters.py b/rl_coach/architectures/head_parameters.py index ccbde7f44..4844f69fb 100644 --- a/rl_coach/architectures/head_parameters.py +++ b/rl_coach/architectures/head_parameters.py @@ -186,3 +186,16 @@ def __init__(self, activation_function: str ='relu', name: str='acer_policy_head dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, loss_weight=loss_weight) + + +class SACPolicyHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='sac_policy_head_params', dense_layer=None): + super().__init__(parameterized_class_name='SACPolicyHead', activation_function=activation_function, name=name, + dense_layer=dense_layer) + +class SACQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='sac_q_head_params', dense_layer=None, + layers_sizes: tuple = (256, 256)): + super().__init__(parameterized_class_name='SACQHead', activation_function=activation_function, name=name, + dense_layer=dense_layer) + self.network_layers_sizes = layers_sizes diff --git a/rl_coach/architectures/tensorflow_components/heads/__init__.py b/rl_coach/architectures/tensorflow_components/heads/__init__.py index daf5492a5..5f3a76c63 100644 --- a/rl_coach/architectures/tensorflow_components/heads/__init__.py +++ b/rl_coach/architectures/tensorflow_components/heads/__init__.py @@ -12,6 +12,8 @@ from .rainbow_q_head import RainbowQHead from .v_head import VHead from .acer_policy_head import ACERPolicyHead +from .sac_head import SACPolicyHead +from .sac_q_head import SACQHead __all__ = [ 'CategoricalQHead', @@ -27,5 +29,7 @@ 'QuantileRegressionQHead', 'RainbowQHead', 'VHead', - 'ACERPolicyHead' + 'ACERPolicyHead', + 'SACPolicyHead', + 'SACQHead' ] diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_head.py new file mode 100644 index 000000000..44ff54529 --- /dev/null +++ b/rl_coach/architectures/tensorflow_components/heads/sac_head.py @@ -0,0 +1,137 @@ +# +# 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.layers import Dense +from rl_coach.architectures.tensorflow_components.heads.head import Head +from rl_coach.architectures.head_parameters import HeadParameters +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import ActionProbabilities +from rl_coach.spaces import SpacesDefinition +from rl_coach.utils import eps + + + +LOG_SIG_CAP_MAX = 2 +LOG_SIG_CAP_MIN = -20 +# LOG_SIG_CAP_MIN = -5 +EPS = 1e-6 + +# moved to head_parameters.py +# class SACPolicyHeadParameters(HeadParameters): +# def __init__(self, activation_function: str ='relu', name: str='policy_head_params', dense_layer=Dense): +# super().__init__(parameterized_class_name='SACPolicyHead', activation_function=activation_function, name=name, +# dense_layer=dense_layer) + + +class SACPolicyHead(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='relu', + squash: bool = True, dense_layer=Dense): + super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, + dense_layer=dense_layer) + self.name = 'sac_policy_head' + self.return_type = ActionProbabilities + self.num_actions = self.spaces.action.shape # continuous actions + self.squash = squash # squashing using tanh + + + + def _build_module(self, input_layer): + self.given_raw_actions = tf.placeholder(tf.float32, [None, self.num_actions], name="actions") + self.input = [self.given_raw_actions] + self.output = [] + + # build the network + self._build_continuous_net(input_layer, self.spaces.action) + + + def _squash_correction(self,actions): + ''' + correct squash operation (in case of bounded actions) according to appendix C in the paper. + NOTE : this correction assume the squash is done with tanh. + :param actions: unbounded actions + :return: the correction to be applied to the log_prob of the actions, assuming tanh squash + ''' + if not self.squash: return 0 + return tf.reduce_sum(tf.log(1 - tf.tanh(actions) ** 2 + EPS), axis=1) + + + def _build_continuous_net(self, input_layer, action_space): + num_actions = action_space.shape + # todo: handle activation + self.policy_mu_and_logsig = self.dense_layer(2*num_actions)(input_layer, name='policy_mu_logsig') + self.policy_mean = tf.identity(self.policy_mu_and_logsig[...,:num_actions],name='policy_mean') + self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[...,num_actions:] + , LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') + + # mean + # self.policy_mean = self.dense_layer(num_actions)(input_layer, name='policy_mean') + # log standard deviation + # policy_log_std = self.dense_layer(num_actions)(input_layer,name='fc_log_std') + # self.policy_log_std = tf.clip_by_value(policy_log_std, LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') + + self.output.append(self.policy_mean) # output[0] + self.output.append(self.policy_log_std) # output[1] + + # define the distributions for the policy + # Tensorflow's multivariate normal distribution supports reparameterization + tfd = tf.contrib.distributions + self.policy_distribution = tfd.MultivariateNormalDiag(loc=self.policy_mean, + scale_diag=tf.exp(self.policy_log_std)) + + # define network outputs + # note that tensorflow supports reparametrization. i.e. policy_action_sample is a tensor through which gradients can flow + + self.raw_actions = self.policy_distribution.sample() + # self.raw_actions = self.policy_distribution.loc # comp_deriv_debug + + if self.squash: + self.actions = tf.tanh(self.raw_actions) + # correct log_prob in case of squash (see appendix C in the paper) + squash_correction_for_sampled = self._squash_correction(self.raw_actions) + squash_correction_for_given = self._squash_correction(self.given_raw_actions) + else: + self.actions = self.raw_actions + squash_correction_for_sampled = 0 + squash_correction_for_given = 0 + + # policy_action_logprob is a tensor through which gradients can flow + self.sampled_actions_logprob = self.policy_distribution.log_prob(self.raw_actions) - squash_correction_for_sampled + + # calculate the log prob of the given actions + self.given_action_logprob = self.policy_distribution.log_prob(self.given_raw_actions) - squash_correction_for_given + self.given_action_logprob_mean = tf.reduce_mean(self.given_action_logprob) + + self.output.append(self.raw_actions) # output[2] : sampled raw action (before squash) + self.output.append(self.actions) # output[3] : squashed (if needed) version of sampled raw_actions + self.output.append(self.given_action_logprob) # output[4]: log prob of given action (squash corrected) + self.output.append(self.given_action_logprob_mean) # output[5]: mean of log prob of given actions (squash corrected) + + # apply weight decay using l2 regularization -> set self.l2_regularization=1e-3 + + # todo in SAC: update the string to represent the head's architecture + def __str__(self): + result = [ + "policy head:" + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = {0})".format(2*self.num_actions), + "policy_mu = output[:num_actions], policy_std = output[num_actions:]" + ] + return '\n'.join(result) diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py new file mode 100644 index 000000000..470e4a14c --- /dev/null +++ b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py @@ -0,0 +1,126 @@ +# +# 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 tensorflow as tf + +from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.head_parameters import HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import QActionStateValue +from rl_coach.spaces import SpacesDefinition, BoxActionSpace + +# moved to head_parameters.py +# class SACQHeadParameters(HeadParameters): +# def __init__(self, activation_function: str ='relu', name: str='q_head_params', dense_layer=Dense, +# layers_sizes: tuple = (256, 256)): +# super().__init__(parameterized_class_name='SACQHead', activation_function=activation_function, name=name, +# dense_layer=dense_layer) +# self.network_layers_sizes = layers_sizes + +class SACQHead(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='relu', + dense_layer=Dense): + super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, + dense_layer=dense_layer) + self.name = 'q_values_head' + if isinstance(self.spaces.action, BoxActionSpace): + self.num_actions = self.spaces.action.shape # continuous actions + else: + raise ValueError( + 'SACQHead does not support action spaces of type: {class_name}'.format( + class_name=self.spaces.action.__class__.__name__, + ) + ) + self.return_type = QActionStateValue + # extract the topology from the SACQHeadParameters + self.network_layers_sizes = agent_parameters.network_wrappers['q'].heads_parameters[0].network_layers_sizes + + def _build_module(self, input_layer): + # SAC Q network is basically 2 networks running in parallel on the same input (state , action) + # state is the observation fed through the input_layer, action is fed through placeholder to the header + # each is calculating q value : q1(s,a) and q2(s,a) + # the output of the head is min(q1,q2) + self.actions = tf.placeholder(tf.float32, [None, self.num_actions], name="actions") + self.target = tf.placeholder(tf.float32, [None,1], name="q_targets") + self.input = [self.actions] + self.output = [] + # Note (1) : in the author's implementation of sac (in rllab) they summarize the embedding of observation and + # action (broadcasting the bias) in the first layer of the network. + + # build q1 network head + with tf.variable_scope("q1_head"): + layer_size = self.network_layers_sizes[0] + qi_obs_emb = self.dense_layer(layer_size)(input_layer,activation=self.activation_function) + qi_act_emb = self.dense_layer(layer_size)(self.actions,activation=self.activation_function) + qi_output = qi_obs_emb + qi_act_emb # merging the inputs by summarizing them (see Note (1)) + for layer_size in self.network_layers_sizes[1:]: + qi_output = self.dense_layer(layer_size)(qi_output, activation=self.activation_function) + # the output layer + self.q1_output = self.dense_layer(1)(qi_output, name='q1_output') + + # build q2 network head + with tf.variable_scope("q2_head"): + layer_size = self.network_layers_sizes[0] + qi_obs_emb = self.dense_layer(layer_size)(input_layer,activation=self.activation_function) + qi_act_emb = self.dense_layer(layer_size)(self.actions,activation=self.activation_function) + qi_output = qi_obs_emb + qi_act_emb # merging the inputs by summarizing them (see Note (1)) + for layer_size in self.network_layers_sizes[1:]: + qi_output = self.dense_layer(layer_size)(qi_output, activation=self.activation_function) + # the output layer + self.q2_output = self.dense_layer(1)(qi_output, name='q2_output') + + # take the minimum as the network's output. this is the log_target (in the original implementation) + self.q_output = tf.minimum(self.q1_output,self.q2_output,name='q_output') + + self.output.append(self.q_output) + + # defining the loss - like in ppo_v_head + # Note that in the parent (Header) loss, it is calculated given the targets per each output. + # here we have targets for 2 interim networks and do not have target for the output (self.q_output) + # so we need to override the loss and calculate it here. see example in PPOVHead + self.q1_loss = 0.5*tf.reduce_mean(tf.square(self.q1_output - self.target)) + self.q2_loss = 0.5*tf.reduce_mean(tf.square(self.q2_output - self.target)) + # eventually both losses are depends on different parameters so we can sum them up + self.loss = self.q1_loss+self.q2_loss + tf.losses.add_loss(self.loss) + + + + + def __str__(self): + result = [ + "q1 output" + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = 1)", + "q2 output" + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = 256)", + "\t\tDense (num outputs = 1)", + "min(Q1,Q2)" + ] + return '\n'.join(result) + + + + + + + + + diff --git a/rl_coach/architectures/tensorflow_components/heads/v_head.py b/rl_coach/architectures/tensorflow_components/heads/v_head.py index 07dbf2538..cb95c8eb4 100644 --- a/rl_coach/architectures/tensorflow_components/heads/v_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/v_head.py @@ -39,8 +39,12 @@ def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, def _build_module(self, input_layer): # Standard V Network - self.output = self.dense_layer(1)(input_layer, name='output', - kernel_initializer=normalized_columns_initializer(1.0)) + # self.output = self.dense_layer(1)(input_layer, name='output', + # kernel_initializer=normalized_columns_initializer(1.0)) + + # for debug SAC - try with default kernel initialization + self.output = self.dense_layer(1)(input_layer, name='output') + def __str__(self): result = [ diff --git a/rl_coach/presets/Mujoco_SAC.py b/rl_coach/presets/Mujoco_SAC.py new file mode 100644 index 000000000..55aeae2cc --- /dev/null +++ b/rl_coach/presets/Mujoco_SAC.py @@ -0,0 +1,138 @@ +from rl_coach.agents.soft_actor_critic_agent import SoftActorCriticAgentParameters +from rl_coach.architectures.layers import Dense +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme +from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps +from rl_coach.filters.filter import InputFilter +from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter +from rl_coach.environments.environment import SingleLevelSelection +from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 +from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager +from rl_coach.graph_managers.graph_manager import ScheduleParameters + +######################## +# my_sac configuration # +######################## + +# some info about the original SAC implementation: +# max_replay_buffer_size = 1e6 +# sampler parameters: +# batch_size = 256 # in each training iteration we have batch_size transitions in the batch +# max_path_length = 1000 # the maximum number of timesteps allowed per path after which the environment is reset +# min_pool_size = 1000 # minimum number of timesteps that should be in the buffer before we can sample a batch +# algorithm parameters: +# discount = 0.99 # the reward discount factor +# lr = 3e-4 # the learning rate that is used to update all networks +# scale_reward = 5 or 20 # the factor to multiply the reward +# tau = 0.005 # soft-update rate of value target network: (1-tau)*target+tau*online +# target_update_interval = 1 # number of iterations between soft updates of target network (1=every iteration) +# epoch_length = 1000 # epoch_length * n_epochs = number of training iterations (steps) +# n_epochs = 3000 # during an epoch we add one sample to the replay buffer and then do (sample a batch + # for training --> learn from batch) iteration for n_train_repeat times + # at the end of each epoch we perform policy evaluation +# n_train_repeats = 1 # number of training iterations (batches) to train per each sample added to buffer +# eval_deterministic = True # whether to use deterministic or stochastic policy for evaluation +# eval_n_episodes = 1 # number of episodes to rollout during evaluation +# n_initial_exploration_steps = 10000 # number of steps to have with initial exploration policy (if defined) +# eval_render = False # whether to render during evaluation + +#------------------ +# mapping to coach parameters +# batch_size --> NetworkParameters.batch_size +# max_path_length --> ??? +# min_pool_size --> schedule_params.heatup_steps +# discount --> AlgorithmParameters.discount +# lr --> NetworkParameters.learning_rate +# scale_reward --> agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5)) +# target_update_interval --> AlgorithmParameters.num_steps_between_copying_online_weights_to_target +# tau --> AlgorithmParameters.rate_for_copying_weights_to_target +# epoch_length --> schedule_params.steps_between_evaluation_periods = EnvironmentSteps(epoch_length) +# n_epochs --> schedule_params.improve_steps ??? +# n_train_repeats --> AlgorithmParameters.num_consecutive_training_steps +# eval_deterministic --> SAC AlgorithmParameters.use_deterministic_for_evaluation +# eval_n_episodes --> schedule_params.evaluation_steps = EnvironmentEpisodes(1) ? check it ! +# n_initial_exploration_steps --> schedule_params.heatup_steps ( with algorithm.heatup_using_network_decisions = False) +# eval_render --> + + + + + +#################### +# Graph Scheduling # + +#################### + +# from DDPG (that is using EpisodicExperienceReplay) +# schedule_params = ScheduleParameters() +# schedule_params.improve_steps = EnvironmentSteps(2000000) +# schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20) +# schedule_params.evaluation_steps = EnvironmentEpisodes(1) +# schedule_params.heatup_steps = EnvironmentSteps(1000) + + + + + +# see graph_manager.py for possible schedule parameters +schedule_params = ScheduleParameters() +schedule_params.improve_steps = EnvironmentSteps(3000000) +# schedule_params.improve_steps = EnvironmentSteps(1000000) +schedule_params.steps_between_evaluation_periods = EnvironmentSteps(1000) +schedule_params.evaluation_steps = EnvironmentEpisodes(1) +schedule_params.heatup_steps = EnvironmentSteps(10000) + + + + +######### +# Agent # +######### +agent_params = SoftActorCriticAgentParameters() +# override default parameters: +# value (v) networks parameters +agent_params.network_wrappers['v'].batch_size = 256 +agent_params.network_wrappers['v'].learning_rate = 0.0003 +agent_params.network_wrappers['v'].middleware_parameters.scheme = [Dense(256)] + +# critic (q) network parameters +agent_params.network_wrappers['q'].heads_parameters[0].network_layers_sizes = (256,256) +agent_params.network_wrappers['q'].batch_size = 256 +agent_params.network_wrappers['q'].learning_rate = 0.0003 + +# actor (policy) network parameters +agent_params.network_wrappers['policy'].batch_size = 256 +agent_params.network_wrappers['policy'].learning_rate = 0.0003 +agent_params.network_wrappers['policy'].middleware_parameters.scheme = [Dense(256)] + +# Algorithm params + + + +# Input Filter +# SAC requires reward scaling for Mujoco environments. +# according to the paper: +# Hopper, Walker-2d, HalfCheetah, Ant - requires scaling of 5 +# Humanoid - requires scaling of 20 + +# disabled for debug only +agent_params.input_filter = InputFilter() +agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5)) + +############### +# Environment # +############### +env_params = GymVectorEnvironment(level=SingleLevelSelection(mujoco_v2)) + +######## +# Test # +######## +preset_validation_params = PresetValidationParameters() +preset_validation_params.test = True +preset_validation_params.min_reward_threshold = 400 +preset_validation_params.max_episodes_to_achieve_reward = 1000 +preset_validation_params.reward_test_level = 'inverted_pendulum' +preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper'] + +graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, + schedule_params=schedule_params, vis_params=VisualizationParameters(), + preset_validation_params=preset_validation_params) From 04f2010bb9450e2a2457db7a2c31d93623180bd8 Mon Sep 17 00:00:00 2001 From: Guy Koren Date: Fri, 5 Apr 2019 12:10:45 +0300 Subject: [PATCH 02/11] SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces --- rl_coach/agents/soft_actor_critic_agent.py | 66 ++++--------------- .../tensorflow_components/heads/sac_head.py | 11 ++-- .../tensorflow_components/heads/sac_q_head.py | 5 ++ rl_coach/environments/gym_environment.py | 2 +- 4 files changed, 27 insertions(+), 57 deletions(-) diff --git a/rl_coach/agents/soft_actor_critic_agent.py b/rl_coach/agents/soft_actor_critic_agent.py index 45007b246..d0cf54d00 100644 --- a/rl_coach/agents/soft_actor_critic_agent.py +++ b/rl_coach/agents/soft_actor_critic_agent.py @@ -130,7 +130,7 @@ def __init__(self): @property def path(self): return 'rl_coach.agents.soft_actor_critic_agent:SoftActorCriticAgent' - # return 'rl_coach.contrib.sac.soft_actor_critic_agent:SoftActorCriticAgent' + # Soft Actor Critic - https://arxiv.org/abs/1801.01290 class SoftActorCriticAgent(PolicyOptimizationAgent): @@ -222,87 +222,55 @@ def learn_from_batch(self, batch): # Actor (policy) Network policy_network = self.networks['policy'].online_network - policy_head = policy_network.output_heads[0] policy_network_keys = self.ap.network_wrappers['policy'].input_embedders_parameters.keys() ########################################## # 1. updating the actor - according to (13) in the paper policy_inputs = copy.copy(batch.states(policy_network_keys)) - policy_results = policy_network.predict(policy_inputs, - outputs=[policy_head.raw_actions,policy_head.actions]) - - sampled_raw_actions,sampled_actions = policy_results - + policy_results = policy_network.predict(policy_inputs) - policy_mu,policy_std = policy_network.predict(policy_inputs, - outputs=[policy_head.policy_mean,policy_head.policy_log_std]) + policy_mu,policy_std,sampled_raw_actions,sampled_actions,sampled_actions_logprob,\ + sampled_actions_logprob_mean = policy_results self.policy_means.add_sample(policy_mu) self.policy_logsig.add_sample(policy_std) - # self.action_signal.add_sample(sampled_actions) - - # now feed the sampled raw actions to get their log probability - policy_inputs['output_0_0'] = sampled_raw_actions - sampled_actions_logprob = policy_network.predict(policy_inputs, - outputs=[policy_head.given_action_logprob]) - self.policy_logprob_sampled.add_sample(sampled_actions_logprob) + self.policy_logprob_sampled.add_sample(sampled_actions_logprob_mean) # get the state-action values for the replayed states and their corresponding actions from the policy - # should we do parallel prediction here ? what does it give us ? q_inputs = copy.copy(batch.states(q_network_keys)) q_inputs['output_0_0'] = sampled_actions - log_target = q_network.predict(q_inputs).squeeze() + log_target = q_network.predict(q_inputs)[0].squeeze() # log internal q values - q1_vals,q2_vals = q_network.predict(q_inputs,outputs=[q_head.q1_output,q_head.q2_output]) + q1_vals, q2_vals = q_network.predict(q_inputs,outputs=[q_head.q1_output,q_head.q2_output]) self.q1_values.add_sample(q1_vals) self.q2_values.add_sample(q2_vals) - - # calculate the gradients according to (13) - # to get grads (unweighted) we need to feed weights of ones and ask for the weighted gradients - # log prob of the given action is index 4 in the sac head's outputs list - - initial_feed_dict = {policy_network.gradients_weights_ph[5]:np.array(1.0)} # get the gradients of log_prob w.r.t the weights (parameters) - indicated as phi in the paper + initial_feed_dict = {policy_network.gradients_weights_ph[5]:np.array(1.0)} dlogp_dphi = policy_network.predict(policy_inputs, outputs=policy_network.weighted_gradients[5], initial_feed_dict=initial_feed_dict) - # the inputs that are defined in the policy head are added to the networks input's dictionary with - # key 'output_0_i' where i is the index of the input. - # in this case, we have given_actions as placeholder and its saved with key output_0_0 - dlogp_dra = policy_network.predict(policy_inputs, - outputs=policy_network.gradients_wrt_inputs[4]['output_0_0']) - dlogp_dra_normalized = np.divide(dlogp_dra,batch.size) - - # dra_dphi is the gradient of the sampled raw actions w.r.t the weights - initial_feed_dict = {policy_network.gradients_weights_ph[2]: dlogp_dra_normalized} - dra_dphi = policy_network.predict(policy_inputs, - outputs=policy_network.weighted_gradients[2], - initial_feed_dict=initial_feed_dict) - # calculate dq_da dq_da = q_network.predict(q_inputs, - outputs=q_network.gradients_wrt_inputs[0]['output_0_0']) - dq_da_normalized = np.divide(dq_da,batch.size) + outputs=q_network.gradients_wrt_inputs[1]['output_0_0']) # calculate da_dphi - initial_feed_dict = {policy_network.gradients_weights_ph[3]: dq_da_normalized} - da_dphi = policy_network.predict(policy_inputs, + initial_feed_dict = {policy_network.gradients_weights_ph[3]: dq_da} + dq_dphi = policy_network.predict(policy_inputs, outputs=policy_network.weighted_gradients[3], initial_feed_dict=initial_feed_dict) - # now given dlogp_dphi, dra_dphi and da_dphi we need to calculate the policy gradients according to (13) - # policy_grads = [(grads_wrt_weights[layer],weights[layer]) for layer in policy_network] - # where: grads_wrt_weights[layer] = dlogp_dphi[layer]+dra_dphi[layer]-da_dphi[layer] - policy_grads = [dlogp_dphi[l]+dra_dphi[l]-da_dphi[l] for l in range(len(dlogp_dphi))] + # now given dlogp_dphi, dq_dphi we need to calculate the policy gradients according to (13) + policy_grads = [dlogp_dphi[l] - dq_dphi[l] for l in range(len(dlogp_dphi))] # apply the gradients to policy networks policy_network.apply_gradients(policy_grads) grads_sumabs=np.sum([np.sum(np.abs(policy_grads[l])) for l in range(len(policy_grads))]) self.policy_grads.add_sample(grads_sumabs) + ########################################## # 2. updating the state value online network weights # done by calculating the targets for the v head according to (5) in the paper @@ -312,13 +280,8 @@ def learn_from_batch(self, batch): self.v_onl_ys.add_sample(value_targets) - # call value_network apply gradients with this target value_loss = value_network.online_network.train_on_batch(value_inputs,value_targets[:,None])[0] - # todo: what do I do with the value_loss ? - - # v_tgt_state = value_network.target_network.predict(value_inputs) # need v_tgt_state for debug - # self.v_tgt_s.add_sample(v_tgt_state) ########################################## # 3. updating the critic (q networks) @@ -344,7 +307,6 @@ def learn_from_batch(self, batch): self.TD_err1.add_sample(q1_loss) self.TD_err2.add_sample(q2_loss) - ########################################## # 4. updating the value target network # I just need to set the parameter rate_for_copying_weights_to_target in the agent parameters to be 1-tau diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_head.py index 44ff54529..f0152a3bb 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_head.py @@ -113,15 +113,18 @@ def _build_continuous_net(self, input_layer, action_space): # policy_action_logprob is a tensor through which gradients can flow self.sampled_actions_logprob = self.policy_distribution.log_prob(self.raw_actions) - squash_correction_for_sampled + self.sampled_actions_logprob_mean = tf.reduce_mean(self.sampled_actions_logprob) # calculate the log prob of the given actions - self.given_action_logprob = self.policy_distribution.log_prob(self.given_raw_actions) - squash_correction_for_given - self.given_action_logprob_mean = tf.reduce_mean(self.given_action_logprob) + # self.given_action_logprob = self.policy_distribution.log_prob(self.given_raw_actions) - squash_correction_for_given + # self.given_action_logprob_mean = tf.reduce_mean(self.given_action_logprob) self.output.append(self.raw_actions) # output[2] : sampled raw action (before squash) self.output.append(self.actions) # output[3] : squashed (if needed) version of sampled raw_actions - self.output.append(self.given_action_logprob) # output[4]: log prob of given action (squash corrected) - self.output.append(self.given_action_logprob_mean) # output[5]: mean of log prob of given actions (squash corrected) + # self.output.append(self.given_action_logprob) # output[4]: log prob of given action (squash corrected) + # self.output.append(self.given_action_logprob_mean) # output[5]: mean of log prob of given actions (squash corrected) + self.output.append(self.sampled_actions_logprob) # output[4]: log prob of sampled action (squash corrected) + self.output.append(self.sampled_actions_logprob_mean) # output[5]: mean of log prob of sampled actions (squash corrected) # apply weight decay using l2 regularization -> set self.l2_regularization=1e-3 diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py index 470e4a14c..bb6ce29c1 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py @@ -86,8 +86,13 @@ def _build_module(self, input_layer): # take the minimum as the network's output. this is the log_target (in the original implementation) self.q_output = tf.minimum(self.q1_output,self.q2_output,name='q_output') + # Note: in both author implementation and spinningup, they use q1 (and not min(q1,q2)) to calculate + # the policy gradients + self.q_output_mean = tf.reduce_mean(self.q1_output) # option 1: use q1 - according to implementations + # self.q_output_mean = tf.reduce_mean(self.q_output) # option 2: use min(q1,q2) according to the paper self.output.append(self.q_output) + self.output.append(self.q_output_mean) # defining the loss - like in ppo_v_head # Note that in the parent (Header) loss, it is calculated given the targets per each output. diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index 63bda2bd5..7762abb61 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -321,7 +321,7 @@ def __init__(self, self.state_space = StateSpace({}) # observations - if not isinstance(self.env.observation_space, gym.spaces.dict.Dict): + if not isinstance(self.env.observation_space, gym.spaces.dict_space.Dict): state_space = {'observation': self.env.observation_space} else: state_space = self.env.observation_space.spaces From 5d873a76972c2c21c6ccf852ccf554561fdee28f Mon Sep 17 00:00:00 2001 From: Guy Koren Date: Sun, 7 Apr 2019 20:04:52 +0300 Subject: [PATCH 03/11] Soft Actor Critic - code cleanup --- rl_coach/agents/soft_actor_critic_agent.py | 52 --------------- .../tensorflow_components/heads/sac_head.py | 36 ++--------- .../tensorflow_components/heads/sac_q_head.py | 17 +---- rl_coach/presets/Mujoco_SAC.py | 63 ------------------- 4 files changed, 8 insertions(+), 160 deletions(-) diff --git a/rl_coach/agents/soft_actor_critic_agent.py b/rl_coach/agents/soft_actor_critic_agent.py index d0cf54d00..d00df52f4 100644 --- a/rl_coach/agents/soft_actor_critic_agent.py +++ b/rl_coach/agents/soft_actor_critic_agent.py @@ -150,55 +150,8 @@ def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAge self.TD_err2 = self.register_signal("TD err2") self.v_tgt_ns = self.register_signal('V_tgt_ns') self.v_onl_ys = self.register_signal('V_onl_ys') - - - self.action_signal = self.register_signal("actions") - def dump_state(self,batch,sampled_raw_actions,sampled_actions): - - # State Value Network - value_network = self.networks['v'] - - # Critic Network - q_network = self.networks['q'].online_network - - # Actor (policy) Network - policy_network = self.networks['policy'].online_network - policy_network_keys = self.ap.network_wrappers['policy'].input_embedders_parameters.keys() - - - # dump batch observations - with open('batch_obs.pkl', 'wb') as f: - pickle.dump(batch.states(policy_network_keys)['observation'], f, pickle.HIGHEST_PROTOCOL) - - # dump sampled actions and raw actions - with open('sampled_actions.pkl', 'wb') as f: - pickle.dump(sampled_raw_actions, f, pickle.HIGHEST_PROTOCOL) - pickle.dump(sampled_actions, f, pickle.HIGHEST_PROTOCOL) - - # dump policy network parameters - polnet = [policy_network.weights[l].eval(policy_network.sess) for l in range(len(policy_network.weights))] - with open('c_policy.pkl', 'wb') as f: - pickle.dump(polnet, f, pickle.HIGHEST_PROTOCOL) - - - - vnet_onl = [value_network.online_network.weights[l].eval(value_network.sess) for l in - range(len(value_network.online_network.weights))] - vnet_tgt = [value_network.target_network.weights[l].eval(value_network.sess) for l in - range(len(value_network.target_network.weights))] - with open('c_v_tgt.pkl', 'wb') as f: - pickle.dump(vnet_tgt, f, pickle.HIGHEST_PROTOCOL) - - with open('c_v_onl.pkl', 'wb') as f: - pickle.dump(vnet_onl, f, pickle.HIGHEST_PROTOCOL) - - qnet = [q_network.weights[l].eval(q_network.sess) for l in range(len(q_network.weights))] - with open('c_q.pkl', 'wb') as f: - pickle.dump(qnet, f, pickle.HIGHEST_PROTOCOL) - - return def learn_from_batch(self, batch): ######################################### @@ -351,16 +304,11 @@ def choose_action(self, curr_state): action_mean,action_sample = result # if using deterministic policy, take the mean values. else, use exploration policy to sample from the pdf - # todo in sac : use the exploration policy to distinguish between training and evaluation. if self.phase == RunPhase.TEST and self.ap.algorithm.use_deterministic_for_evaluation: action = action_mean[0] else: action = action_sample[0] - # todo in SAC: delete the below. kept for reference during debug - # action_values = actor_network.predict(tf_input_state).squeeze() - # action = self.exploration_policy.get_action(action_values) - self.action_signal.add_sample(action) action_info = ActionInfo(action=action) diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_head.py index f0152a3bb..7b7d12e4f 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_head.py @@ -19,26 +19,16 @@ from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.architectures.tensorflow_components.heads.head import Head -from rl_coach.architectures.head_parameters import HeadParameters from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import SpacesDefinition -from rl_coach.utils import eps LOG_SIG_CAP_MAX = 2 LOG_SIG_CAP_MIN = -20 -# LOG_SIG_CAP_MIN = -5 EPS = 1e-6 -# moved to head_parameters.py -# class SACPolicyHeadParameters(HeadParameters): -# def __init__(self, activation_function: str ='relu', name: str='policy_head_params', dense_layer=Dense): -# super().__init__(parameterized_class_name='SACPolicyHead', activation_function=activation_function, name=name, -# dense_layer=dense_layer) - - class SACPolicyHead(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='relu', @@ -77,14 +67,8 @@ def _build_continuous_net(self, input_layer, action_space): # todo: handle activation self.policy_mu_and_logsig = self.dense_layer(2*num_actions)(input_layer, name='policy_mu_logsig') self.policy_mean = tf.identity(self.policy_mu_and_logsig[...,:num_actions],name='policy_mean') - self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[...,num_actions:] - , LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') - - # mean - # self.policy_mean = self.dense_layer(num_actions)(input_layer, name='policy_mean') - # log standard deviation - # policy_log_std = self.dense_layer(num_actions)(input_layer,name='fc_log_std') - # self.policy_log_std = tf.clip_by_value(policy_log_std, LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') + self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[...,num_actions:], + LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') self.output.append(self.policy_mean) # output[0] self.output.append(self.policy_log_std) # output[1] @@ -99,36 +83,26 @@ def _build_continuous_net(self, input_layer, action_space): # note that tensorflow supports reparametrization. i.e. policy_action_sample is a tensor through which gradients can flow self.raw_actions = self.policy_distribution.sample() - # self.raw_actions = self.policy_distribution.loc # comp_deriv_debug if self.squash: self.actions = tf.tanh(self.raw_actions) # correct log_prob in case of squash (see appendix C in the paper) - squash_correction_for_sampled = self._squash_correction(self.raw_actions) - squash_correction_for_given = self._squash_correction(self.given_raw_actions) + squash_correction = self._squash_correction(self.raw_actions) else: self.actions = self.raw_actions - squash_correction_for_sampled = 0 - squash_correction_for_given = 0 + squash_correction = 0 # policy_action_logprob is a tensor through which gradients can flow - self.sampled_actions_logprob = self.policy_distribution.log_prob(self.raw_actions) - squash_correction_for_sampled + self.sampled_actions_logprob = self.policy_distribution.log_prob(self.raw_actions) - squash_correction self.sampled_actions_logprob_mean = tf.reduce_mean(self.sampled_actions_logprob) - # calculate the log prob of the given actions - # self.given_action_logprob = self.policy_distribution.log_prob(self.given_raw_actions) - squash_correction_for_given - # self.given_action_logprob_mean = tf.reduce_mean(self.given_action_logprob) - self.output.append(self.raw_actions) # output[2] : sampled raw action (before squash) self.output.append(self.actions) # output[3] : squashed (if needed) version of sampled raw_actions - # self.output.append(self.given_action_logprob) # output[4]: log prob of given action (squash corrected) - # self.output.append(self.given_action_logprob_mean) # output[5]: mean of log prob of given actions (squash corrected) self.output.append(self.sampled_actions_logprob) # output[4]: log prob of sampled action (squash corrected) self.output.append(self.sampled_actions_logprob_mean) # output[5]: mean of log prob of sampled actions (squash corrected) # apply weight decay using l2 regularization -> set self.l2_regularization=1e-3 - # todo in SAC: update the string to represent the head's architecture def __str__(self): result = [ "policy head:" diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py index bb6ce29c1..63ad296a6 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py @@ -23,14 +23,6 @@ from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition, BoxActionSpace -# moved to head_parameters.py -# class SACQHeadParameters(HeadParameters): -# def __init__(self, activation_function: str ='relu', name: str='q_head_params', dense_layer=Dense, -# layers_sizes: tuple = (256, 256)): -# super().__init__(parameterized_class_name='SACQHead', activation_function=activation_function, name=name, -# dense_layer=dense_layer) -# self.network_layers_sizes = layers_sizes - class SACQHead(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='relu', @@ -88,16 +80,13 @@ def _build_module(self, input_layer): self.q_output = tf.minimum(self.q1_output,self.q2_output,name='q_output') # Note: in both author implementation and spinningup, they use q1 (and not min(q1,q2)) to calculate # the policy gradients - self.q_output_mean = tf.reduce_mean(self.q1_output) # option 1: use q1 - according to implementations - # self.q_output_mean = tf.reduce_mean(self.q_output) # option 2: use min(q1,q2) according to the paper + # self.q_output_mean = tf.reduce_mean(self.q1_output) # option 1: use q1 - according to implementations + self.q_output_mean = tf.reduce_mean(self.q_output) # option 2: use min(q1,q2) according to the paper self.output.append(self.q_output) self.output.append(self.q_output_mean) - # defining the loss - like in ppo_v_head - # Note that in the parent (Header) loss, it is calculated given the targets per each output. - # here we have targets for 2 interim networks and do not have target for the output (self.q_output) - # so we need to override the loss and calculate it here. see example in PPOVHead + # defining the loss self.q1_loss = 0.5*tf.reduce_mean(tf.square(self.q1_output - self.target)) self.q2_loss = 0.5*tf.reduce_mean(tf.square(self.q2_output - self.target)) # eventually both losses are depends on different parameters so we can sum them up diff --git a/rl_coach/presets/Mujoco_SAC.py b/rl_coach/presets/Mujoco_SAC.py index 55aeae2cc..b1ea1b4a2 100644 --- a/rl_coach/presets/Mujoco_SAC.py +++ b/rl_coach/presets/Mujoco_SAC.py @@ -9,81 +9,19 @@ from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager from rl_coach.graph_managers.graph_manager import ScheduleParameters -######################## -# my_sac configuration # -######################## - -# some info about the original SAC implementation: -# max_replay_buffer_size = 1e6 -# sampler parameters: -# batch_size = 256 # in each training iteration we have batch_size transitions in the batch -# max_path_length = 1000 # the maximum number of timesteps allowed per path after which the environment is reset -# min_pool_size = 1000 # minimum number of timesteps that should be in the buffer before we can sample a batch -# algorithm parameters: -# discount = 0.99 # the reward discount factor -# lr = 3e-4 # the learning rate that is used to update all networks -# scale_reward = 5 or 20 # the factor to multiply the reward -# tau = 0.005 # soft-update rate of value target network: (1-tau)*target+tau*online -# target_update_interval = 1 # number of iterations between soft updates of target network (1=every iteration) -# epoch_length = 1000 # epoch_length * n_epochs = number of training iterations (steps) -# n_epochs = 3000 # during an epoch we add one sample to the replay buffer and then do (sample a batch - # for training --> learn from batch) iteration for n_train_repeat times - # at the end of each epoch we perform policy evaluation -# n_train_repeats = 1 # number of training iterations (batches) to train per each sample added to buffer -# eval_deterministic = True # whether to use deterministic or stochastic policy for evaluation -# eval_n_episodes = 1 # number of episodes to rollout during evaluation -# n_initial_exploration_steps = 10000 # number of steps to have with initial exploration policy (if defined) -# eval_render = False # whether to render during evaluation - -#------------------ -# mapping to coach parameters -# batch_size --> NetworkParameters.batch_size -# max_path_length --> ??? -# min_pool_size --> schedule_params.heatup_steps -# discount --> AlgorithmParameters.discount -# lr --> NetworkParameters.learning_rate -# scale_reward --> agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5)) -# target_update_interval --> AlgorithmParameters.num_steps_between_copying_online_weights_to_target -# tau --> AlgorithmParameters.rate_for_copying_weights_to_target -# epoch_length --> schedule_params.steps_between_evaluation_periods = EnvironmentSteps(epoch_length) -# n_epochs --> schedule_params.improve_steps ??? -# n_train_repeats --> AlgorithmParameters.num_consecutive_training_steps -# eval_deterministic --> SAC AlgorithmParameters.use_deterministic_for_evaluation -# eval_n_episodes --> schedule_params.evaluation_steps = EnvironmentEpisodes(1) ? check it ! -# n_initial_exploration_steps --> schedule_params.heatup_steps ( with algorithm.heatup_using_network_decisions = False) -# eval_render --> - - - - #################### # Graph Scheduling # - #################### -# from DDPG (that is using EpisodicExperienceReplay) -# schedule_params = ScheduleParameters() -# schedule_params.improve_steps = EnvironmentSteps(2000000) -# schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(20) -# schedule_params.evaluation_steps = EnvironmentEpisodes(1) -# schedule_params.heatup_steps = EnvironmentSteps(1000) - - - - - # see graph_manager.py for possible schedule parameters schedule_params = ScheduleParameters() schedule_params.improve_steps = EnvironmentSteps(3000000) -# schedule_params.improve_steps = EnvironmentSteps(1000000) schedule_params.steps_between_evaluation_periods = EnvironmentSteps(1000) schedule_params.evaluation_steps = EnvironmentEpisodes(1) schedule_params.heatup_steps = EnvironmentSteps(10000) - - ######### # Agent # ######### @@ -117,7 +55,6 @@ # disabled for debug only agent_params.input_filter = InputFilter() agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5)) - ############### # Environment # ############### From b90da84a451e84e8f0a8ca3e0310c41a1954e0d1 Mon Sep 17 00:00:00 2001 From: Shadi Endrawis Date: Wed, 17 Apr 2019 17:08:32 +0300 Subject: [PATCH 04/11] code cleanup --- rl_coach/agents/soft_actor_critic_agent.py | 34 ++++++++----------- .../tensorflow_components/heads/sac_head.py | 27 ++++++--------- .../tensorflow_components/heads/sac_q_head.py | 22 +++++------- .../tensorflow_components/heads/v_head.py | 1 - rl_coach/presets/Mujoco_SAC.py | 12 +++---- 5 files changed, 38 insertions(+), 58 deletions(-) diff --git a/rl_coach/agents/soft_actor_critic_agent.py b/rl_coach/agents/soft_actor_critic_agent.py index d00df52f4..b945f4a52 100644 --- a/rl_coach/agents/soft_actor_critic_agent.py +++ b/rl_coach/agents/soft_actor_critic_agent.py @@ -18,18 +18,15 @@ import copy import numpy as np from collections import OrderedDict -import pickle from rl_coach.agents.agent import Agent from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent -# import heads from rl_coach.architectures.head_parameters import SACQHeadParameters,SACPolicyHeadParameters,VHeadParameters from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters -# from rl_coach.logger import screen from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.spaces import BoxActionSpace @@ -79,7 +76,7 @@ def __init__(self): self.input_embedders_parameters = {'observation': InputEmbedderParameters(scheme=EmbedderScheme.Empty)} self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty) self.heads_parameters = [SACQHeadParameters()] # SACQHeadParameters includes the topology of the head - self.rescale_gradient_from_head_by_factor = [1] # todo: its not in the NetworkParameters base. shouldnt it be ? Yes ! + self.rescale_gradient_from_head_by_factor = [1] self.optimizer_type = 'Adam' self.batch_size = 256 self.async_training = False @@ -120,7 +117,7 @@ def __init__(self): class SoftActorCriticAgentParameters(AgentParameters): def __init__(self): super().__init__(algorithm=SoftActorCriticAlgorithmParameters(), - exploration=AdditiveNoiseParameters(), # todo in SAC: should be re-examined + exploration=AdditiveNoiseParameters(), memory=ExperienceReplayParameters(), # SAC doesnt use episodic related data # network wrappers: networks=OrderedDict([("policy", SACPolicyNetworkParameters()), @@ -152,7 +149,6 @@ def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAge self.v_onl_ys = self.register_signal('V_onl_ys') self.action_signal = self.register_signal("actions") - def learn_from_batch(self, batch): ######################################### # need to update the following networks: @@ -182,7 +178,7 @@ def learn_from_batch(self, batch): policy_inputs = copy.copy(batch.states(policy_network_keys)) policy_results = policy_network.predict(policy_inputs) - policy_mu,policy_std,sampled_raw_actions,sampled_actions,sampled_actions_logprob,\ + policy_mu, policy_std, sampled_raw_actions, sampled_actions, sampled_actions_logprob, \ sampled_actions_logprob_mean = policy_results self.policy_means.add_sample(policy_mu) @@ -195,13 +191,13 @@ def learn_from_batch(self, batch): log_target = q_network.predict(q_inputs)[0].squeeze() # log internal q values - q1_vals, q2_vals = q_network.predict(q_inputs,outputs=[q_head.q1_output,q_head.q2_output]) + q1_vals, q2_vals = q_network.predict(q_inputs, outputs=[q_head.q1_output, q_head.q2_output]) self.q1_values.add_sample(q1_vals) self.q2_values.add_sample(q2_vals) # calculate the gradients according to (13) # get the gradients of log_prob w.r.t the weights (parameters) - indicated as phi in the paper - initial_feed_dict = {policy_network.gradients_weights_ph[5]:np.array(1.0)} + initial_feed_dict = {policy_network.gradients_weights_ph[5]: np.array(1.0)} dlogp_dphi = policy_network.predict(policy_inputs, outputs=policy_network.weighted_gradients[5], initial_feed_dict=initial_feed_dict) @@ -221,7 +217,7 @@ def learn_from_batch(self, batch): # apply the gradients to policy networks policy_network.apply_gradients(policy_grads) - grads_sumabs=np.sum([np.sum(np.abs(policy_grads[l])) for l in range(len(policy_grads))]) + grads_sumabs = np.sum([np.sum(np.abs(policy_grads[l])) for l in range(len(policy_grads))]) self.policy_grads.add_sample(grads_sumabs) ########################################## @@ -234,7 +230,7 @@ def learn_from_batch(self, batch): self.v_onl_ys.add_sample(value_targets) # call value_network apply gradients with this target - value_loss = value_network.online_network.train_on_batch(value_inputs,value_targets[:,None])[0] + value_loss = value_network.online_network.train_on_batch(value_inputs, value_targets[:,None])[0] ########################################## # 3. updating the critic (q networks) @@ -254,9 +250,9 @@ def learn_from_batch(self, batch): (1.0 - batch.game_overs(expand_dims=True)) * self.ap.algorithm.discount * v_target_next_state # call critic network update - result = q_network.train_on_batch(q_inputs,TD_targets,additional_fetches=[q_head.q1_loss,q_head.q2_loss]) + result = q_network.train_on_batch(q_inputs, TD_targets, additional_fetches=[q_head.q1_loss, q_head.q2_loss]) total_loss, losses, unclipped_grads = result[:3] - q1_loss,q2_loss = result[3] + q1_loss, q2_loss = result[3] self.TD_err1.add_sample(q1_loss) self.TD_err2.add_sample(q2_loss) @@ -268,15 +264,14 @@ def learn_from_batch(self, batch): return total_loss, losses, unclipped_grads def get_prediction(self, states): - ''' + """ get the mean and stdev of the policy distribution given 'states' :param states: the states for which we need to sample actions from the policy :return: mean and stdev ? what about logprob ? - ''' + """ tf_input_state = self.prepare_batch_for_inference(states, 'policy') return self.networks['policy'].online_network.predict(tf_input_state) - def train(self): # since the algorithm works with experience replay buffer (non-episodic), # we cant use the policy optimization train method. we need Agent.train @@ -284,14 +279,13 @@ def train(self): return Agent.train(self) def choose_action(self, curr_state): - ''' + """ choose_action - chooses the most likely action if 'deterministic' - take the mean of the policy which is the prediction of the policy network. else - use the exploration policy :param curr_state: :return: action wrapped in ActionInfo - ''' - + """ if not isinstance(self.spaces.action, BoxActionSpace): raise ValueError("SAC works only for continuous control problems") # convert to batch so we can run it through the network @@ -300,7 +294,7 @@ def choose_action(self, curr_state): policy_network = self.networks['policy'].online_network policy_head = policy_network.output_heads[0] result = policy_network.predict(tf_input_state, - outputs=[policy_head.policy_mean,policy_head.actions]) + outputs=[policy_head.policy_mean, policy_head.actions]) action_mean,action_sample = result # if using deterministic policy, take the mean values. else, use exploration policy to sample from the pdf diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_head.py index 7b7d12e4f..3f85a3c25 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_head.py @@ -14,7 +14,6 @@ # limitations under the License. # -import numpy as np import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense @@ -22,12 +21,11 @@ from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import SpacesDefinition - - +from rl_coach.utils import eps LOG_SIG_CAP_MAX = 2 LOG_SIG_CAP_MIN = -20 -EPS = 1e-6 + class SACPolicyHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, @@ -40,8 +38,6 @@ def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, self.num_actions = self.spaces.action.shape # continuous actions self.squash = squash # squashing using tanh - - def _build_module(self, input_layer): self.given_raw_actions = tf.placeholder(tf.float32, [None, self.num_actions], name="actions") self.input = [self.given_raw_actions] @@ -50,7 +46,6 @@ def _build_module(self, input_layer): # build the network self._build_continuous_net(input_layer, self.spaces.action) - def _squash_correction(self,actions): ''' correct squash operation (in case of bounded actions) according to appendix C in the paper. @@ -58,16 +53,16 @@ def _squash_correction(self,actions): :param actions: unbounded actions :return: the correction to be applied to the log_prob of the actions, assuming tanh squash ''' - if not self.squash: return 0 - return tf.reduce_sum(tf.log(1 - tf.tanh(actions) ** 2 + EPS), axis=1) - + if not self.squash: + return 0 + return tf.reduce_sum(tf.log(1 - tf.tanh(actions) ** 2 + eps), axis=1) def _build_continuous_net(self, input_layer, action_space): - num_actions = action_space.shape - # todo: handle activation + num_actions = action_space.shape[0] + self.policy_mu_and_logsig = self.dense_layer(2*num_actions)(input_layer, name='policy_mu_logsig') - self.policy_mean = tf.identity(self.policy_mu_and_logsig[...,:num_actions],name='policy_mean') - self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[...,num_actions:], + self.policy_mean = tf.identity(self.policy_mu_and_logsig[..., :num_actions], name='policy_mean') + self.policy_log_std = tf.clip_by_value(self.policy_mu_and_logsig[..., num_actions:], LOG_SIG_CAP_MIN, LOG_SIG_CAP_MAX,name='policy_log_std') self.output.append(self.policy_mean) # output[0] @@ -80,8 +75,8 @@ def _build_continuous_net(self, input_layer, action_space): scale_diag=tf.exp(self.policy_log_std)) # define network outputs - # note that tensorflow supports reparametrization. i.e. policy_action_sample is a tensor through which gradients can flow - + # note that tensorflow supports reparametrization. + # i.e. policy_action_sample is a tensor through which gradients can flow self.raw_actions = self.policy_distribution.sample() if self.squash: diff --git a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py index 63ad296a6..0f0352f64 100644 --- a/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/sac_q_head.py @@ -17,12 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.head_parameters import HeadParameters from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition, BoxActionSpace + class SACQHead(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='relu', @@ -48,7 +48,7 @@ def _build_module(self, input_layer): # each is calculating q value : q1(s,a) and q2(s,a) # the output of the head is min(q1,q2) self.actions = tf.placeholder(tf.float32, [None, self.num_actions], name="actions") - self.target = tf.placeholder(tf.float32, [None,1], name="q_targets") + self.target = tf.placeholder(tf.float32, [None, 1], name="q_targets") self.input = [self.actions] self.output = [] # Note (1) : in the author's implementation of sac (in rllab) they summarize the embedding of observation and @@ -57,8 +57,8 @@ def _build_module(self, input_layer): # build q1 network head with tf.variable_scope("q1_head"): layer_size = self.network_layers_sizes[0] - qi_obs_emb = self.dense_layer(layer_size)(input_layer,activation=self.activation_function) - qi_act_emb = self.dense_layer(layer_size)(self.actions,activation=self.activation_function) + qi_obs_emb = self.dense_layer(layer_size)(input_layer, activation=self.activation_function) + qi_act_emb = self.dense_layer(layer_size)(self.actions, activation=self.activation_function) qi_output = qi_obs_emb + qi_act_emb # merging the inputs by summarizing them (see Note (1)) for layer_size in self.network_layers_sizes[1:]: qi_output = self.dense_layer(layer_size)(qi_output, activation=self.activation_function) @@ -68,8 +68,8 @@ def _build_module(self, input_layer): # build q2 network head with tf.variable_scope("q2_head"): layer_size = self.network_layers_sizes[0] - qi_obs_emb = self.dense_layer(layer_size)(input_layer,activation=self.activation_function) - qi_act_emb = self.dense_layer(layer_size)(self.actions,activation=self.activation_function) + qi_obs_emb = self.dense_layer(layer_size)(input_layer, activation=self.activation_function) + qi_act_emb = self.dense_layer(layer_size)(self.actions, activation=self.activation_function) qi_output = qi_obs_emb + qi_act_emb # merging the inputs by summarizing them (see Note (1)) for layer_size in self.network_layers_sizes[1:]: qi_output = self.dense_layer(layer_size)(qi_output, activation=self.activation_function) @@ -77,11 +77,10 @@ def _build_module(self, input_layer): self.q2_output = self.dense_layer(1)(qi_output, name='q2_output') # take the minimum as the network's output. this is the log_target (in the original implementation) - self.q_output = tf.minimum(self.q1_output,self.q2_output,name='q_output') - # Note: in both author implementation and spinningup, they use q1 (and not min(q1,q2)) to calculate + self.q_output = tf.minimum(self.q1_output, self.q2_output, name='q_output') # the policy gradients - # self.q_output_mean = tf.reduce_mean(self.q1_output) # option 1: use q1 - according to implementations - self.q_output_mean = tf.reduce_mean(self.q_output) # option 2: use min(q1,q2) according to the paper + # self.q_output_mean = tf.reduce_mean(self.q1_output) # option 1: use q1 + self.q_output_mean = tf.reduce_mean(self.q_output) # option 2: use min(q1,q2) self.output.append(self.q_output) self.output.append(self.q_output_mean) @@ -93,9 +92,6 @@ def _build_module(self, input_layer): self.loss = self.q1_loss+self.q2_loss tf.losses.add_loss(self.loss) - - - def __str__(self): result = [ "q1 output" diff --git a/rl_coach/architectures/tensorflow_components/heads/v_head.py b/rl_coach/architectures/tensorflow_components/heads/v_head.py index cb95c8eb4..939a1f651 100644 --- a/rl_coach/architectures/tensorflow_components/heads/v_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/v_head.py @@ -45,7 +45,6 @@ def _build_module(self, input_layer): # for debug SAC - try with default kernel initialization self.output = self.dense_layer(1)(input_layer, name='output') - def __str__(self): result = [ "Dense (num outputs = 1)" diff --git a/rl_coach/presets/Mujoco_SAC.py b/rl_coach/presets/Mujoco_SAC.py index b1ea1b4a2..23cc62b7a 100644 --- a/rl_coach/presets/Mujoco_SAC.py +++ b/rl_coach/presets/Mujoco_SAC.py @@ -1,7 +1,7 @@ from rl_coach.agents.soft_actor_critic_agent import SoftActorCriticAgentParameters from rl_coach.architectures.layers import Dense -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme -from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps from rl_coach.filters.filter import InputFilter from rl_coach.filters.reward.reward_rescale_filter import RewardRescaleFilter from rl_coach.environments.environment import SingleLevelSelection @@ -42,19 +42,15 @@ agent_params.network_wrappers['policy'].learning_rate = 0.0003 agent_params.network_wrappers['policy'].middleware_parameters.scheme = [Dense(256)] -# Algorithm params - - - # Input Filter # SAC requires reward scaling for Mujoco environments. # according to the paper: # Hopper, Walker-2d, HalfCheetah, Ant - requires scaling of 5 # Humanoid - requires scaling of 20 -# disabled for debug only agent_params.input_filter = InputFilter() agent_params.input_filter.add_reward_filter('rescale', RewardRescaleFilter(5)) + ############### # Environment # ############### @@ -66,7 +62,7 @@ preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = 400 -preset_validation_params.max_episodes_to_achieve_reward = 1000 +preset_validation_params.max_episodes_to_achieve_reward = 2200 preset_validation_params.reward_test_level = 'inverted_pendulum' preset_validation_params.trace_test_levels = ['inverted_pendulum', 'hopper'] From 3ab724d04119d99af09e8ecc298df782013b4968 Mon Sep 17 00:00:00 2001 From: Shadi Endrawis Date: Mon, 22 Apr 2019 19:18:09 +0300 Subject: [PATCH 05/11] V-head initialization fix --- rl_coach/agents/soft_actor_critic_agent.py | 2 +- rl_coach/architectures/head_parameters.py | 4 ++-- .../tensorflow_components/heads/v_head.py | 14 ++++++++------ rl_coach/environments/gym_environment.py | 2 +- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/rl_coach/agents/soft_actor_critic_agent.py b/rl_coach/agents/soft_actor_critic_agent.py index b945f4a52..20db98775 100644 --- a/rl_coach/agents/soft_actor_critic_agent.py +++ b/rl_coach/agents/soft_actor_critic_agent.py @@ -59,7 +59,7 @@ def __init__(self): super().__init__() self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='relu')} self.middleware_parameters = FCMiddlewareParameters(activation_function='relu') - self.heads_parameters = [VHeadParameters()] + self.heads_parameters = [VHeadParameters(initializer='xavier')] self.rescale_gradient_from_head_by_factor = [1] self.optimizer_type = 'Adam' self.batch_size = 256 diff --git a/rl_coach/architectures/head_parameters.py b/rl_coach/architectures/head_parameters.py index d901fc28e..3b74b1539 100644 --- a/rl_coach/architectures/head_parameters.py +++ b/rl_coach/architectures/head_parameters.py @@ -36,7 +36,6 @@ def path(self): return 'rl_coach.architectures.tensorflow_components.heads:' + self.parameterized_class_name - class PPOHeadParameters(HeadParameters): def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params', num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, @@ -50,7 +49,7 @@ def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params' class VHeadParameters(HeadParameters): def __init__(self, activation_function: str ='relu', name: str='v_head_params', num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=None): + loss_weight: float = 1.0, dense_layer=None, initializer='normalized_columns'): super().__init__(parameterized_class_name="VHead", activation_function=activation_function, name=name, dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, @@ -203,6 +202,7 @@ def __init__(self, activation_function: str ='relu', name: str='sac_policy_head_ super().__init__(parameterized_class_name='SACPolicyHead', activation_function=activation_function, name=name, dense_layer=dense_layer) + class SACQHeadParameters(HeadParameters): def __init__(self, activation_function: str ='relu', name: str='sac_q_head_params', dense_layer=None, layers_sizes: tuple = (256, 256)): diff --git a/rl_coach/architectures/tensorflow_components/heads/v_head.py b/rl_coach/architectures/tensorflow_components/heads/v_head.py index 939a1f651..62bfba03b 100644 --- a/rl_coach/architectures/tensorflow_components/heads/v_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/v_head.py @@ -26,7 +26,7 @@ class VHead(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='relu', - dense_layer=Dense): + dense_layer=Dense, initializer='normalized_columns'): super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, dense_layer=dense_layer) self.name = 'v_values_head' @@ -37,13 +37,15 @@ def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, else: self.loss_type = tf.losses.mean_squared_error + self.initializer = initializer + def _build_module(self, input_layer): # Standard V Network - # self.output = self.dense_layer(1)(input_layer, name='output', - # kernel_initializer=normalized_columns_initializer(1.0)) - - # for debug SAC - try with default kernel initialization - self.output = self.dense_layer(1)(input_layer, name='output') + if self.initializer == 'normalized_columns': + self.output = self.dense_layer(1)(input_layer, name='output', + kernel_initializer=normalized_columns_initializer(1.0)) + elif self.initializer == 'xavier' or self.initializer is None: + self.output = self.dense_layer(1)(input_layer, name='output') def __str__(self): result = [ diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index 7762abb61..63bda2bd5 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -321,7 +321,7 @@ def __init__(self, self.state_space = StateSpace({}) # observations - if not isinstance(self.env.observation_space, gym.spaces.dict_space.Dict): + if not isinstance(self.env.observation_space, gym.spaces.dict.Dict): state_space = {'observation': self.env.observation_space} else: state_space = self.env.observation_space.spaces From 716aa788b115db1312bdf6a63a01e39ce7fa14ac Mon Sep 17 00:00:00 2001 From: Shadi Endrawis Date: Wed, 24 Apr 2019 12:29:10 +0300 Subject: [PATCH 06/11] SAC benchmarks --- benchmarks/README.md | 1 + benchmarks/clipped_ppo/README.md | 2 +- benchmarks/ddpg/README.md | 2 +- benchmarks/sac/README.md | 48 +++++++++++++++++++++ benchmarks/sac/half_cheetah_sac.png | Bin 0 -> 67180 bytes benchmarks/sac/hopper_sac.png | Bin 0 -> 99681 bytes benchmarks/sac/humanoid_sac.png | Bin 0 -> 91725 bytes benchmarks/sac/inverted_pendulum_sac.png | Bin 0 -> 50099 bytes benchmarks/sac/walker2d_sac.png | Bin 0 -> 78440 bytes docs_raw/source/_static/img/algorithms.png | Bin 51768 -> 60448 bytes docs_raw/source/diagrams.xml | 2 +- img/algorithms.png | Bin 51768 -> 60448 bytes rl_coach/architectures/head_parameters.py | 1 + rl_coach/environments/gym_environment.py | 2 +- 14 files changed, 54 insertions(+), 4 deletions(-) create mode 100644 benchmarks/sac/README.md create mode 100644 benchmarks/sac/half_cheetah_sac.png create mode 100644 benchmarks/sac/hopper_sac.png create mode 100644 benchmarks/sac/humanoid_sac.png create mode 100644 benchmarks/sac/inverted_pendulum_sac.png create mode 100644 benchmarks/sac/walker2d_sac.png diff --git a/benchmarks/README.md b/benchmarks/README.md index 603e8823e..fe22f89a5 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -37,6 +37,7 @@ The environments that were used for testing include: |**[ACER](acer)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Atari | | |**[Clipped PPO](clipped_ppo)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | |**[DDPG](ddpg)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | +|**[SAC](sac)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Mujoco | | |**[NEC](nec)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Atari | | |**[HER](ddpg_her)** | ![#2E8B57](https://placehold.it/15/2E8B57/000000?text=+) |Fetch | | |**[DFP](dfp)** | ![#ceffad](https://placehold.it/15/ceffad/000000?text=+) |Doom | Doom Battle was not verified | diff --git a/benchmarks/clipped_ppo/README.md b/benchmarks/clipped_ppo/README.md index 9eedf095f..f29152add 100644 --- a/benchmarks/clipped_ppo/README.md +++ b/benchmarks/clipped_ppo/README.md @@ -1,6 +1,6 @@ # Clipped PPO -Each experiment uses 3 seeds and is trained for 10k environment steps. +Each experiment uses 3 seeds and is trained for 10M environment steps. The parameters used for Clipped PPO are the same parameters as described in the [original paper](https://arxiv.org/abs/1707.06347). ### Inverted Pendulum Clipped PPO - single worker diff --git a/benchmarks/ddpg/README.md b/benchmarks/ddpg/README.md index 0163d09e1..59a5cdff2 100644 --- a/benchmarks/ddpg/README.md +++ b/benchmarks/ddpg/README.md @@ -1,6 +1,6 @@ # DDPG -Each experiment uses 3 seeds and is trained for 2k environment steps. +Each experiment uses 3 seeds and is trained for 2M environment steps. The parameters used for DDPG are the same parameters as described in the [original paper](https://arxiv.org/abs/1509.02971). ### Inverted Pendulum DDPG - single worker diff --git a/benchmarks/sac/README.md b/benchmarks/sac/README.md new file mode 100644 index 000000000..50e7b6ebc --- /dev/null +++ b/benchmarks/sac/README.md @@ -0,0 +1,48 @@ +# Soft Actor Critic + +Each experiment uses 3 seeds and is trained for 3M environment steps. +The parameters used for SAC are the same parameters as described in the [original paper](https://arxiv.org/abs/1801.01290). + +### Inverted Pendulum SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl inverted_pendulum +``` + +Inverted Pendulum SAC + + +### Hopper Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl hopper +``` + +Hopper SAC + + +### Half Cheetah Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl half_cheetah +``` + +Half Cheetah SAC + + +### Walker 2D Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl walker2d +``` + +Walker 2D SAC + + +### Humanoid Clipped SAC - single worker + +```bash +coach -p Mujoco_SAC -lvl humanoid +``` + +Humanoid SAC diff --git a/benchmarks/sac/half_cheetah_sac.png b/benchmarks/sac/half_cheetah_sac.png new file mode 100644 index 0000000000000000000000000000000000000000..00b18b54d4c11d781132c13350d75ce83a7d16be GIT binary patch literal 67180 zcmaHTby!sG*7wW+Qqo9BN+T&CT`CAjNeE z9?yB+bKY~F_q#6sP++s~z4lt`{>8c{`lX5-9?pFn5D0{)ATOg10-?2oK#(A;JHU4| zkVMZwAbOC3%o9y-qunpq-Ud*g+uJOErnpnXx?1n(EQ%gMCDS*UOcDsrN74JLdQQCgg-T&f#JM5RQ zW}*9|#9GjQ_?Uut$KlLXN6M0JcGu~gH~hiIVmNk z%)v4Y4;HWPdE97UU`17wD1E!Q*5As6K7uc{F}!}xEK&cRrhfKIZEI`mTiTf6rx2!> zEaUrFTm^HzejIb(>EARtRMhszl4^Da1+uR|}1#x6j8$e14_tnQ?V2y!yus$dkxVF~)=54ti zWZS+FRBZj740S6yFiu6sqe2izm?Bu4fEd-xp}kPQx!5K@zHbKw72 zsgM#{ZVVj}bDS`}u2scWJM2&8S&)+KNBZ<&;wcz9TRToqCZvQA_#FuYoC>#YQi0m2 zzEs#a@k`@ND>4(q$x9rDK}rJPl^DdII3c7<3<<2jg`h(W zD3|&Fytuz!i4s^_YG4Y2tw{#PaY^h+0xkrdhZMsfAyIi?XTNdvC#@${zy+Y<97~5_ 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num_output_head_copies=num_output_head_copies, rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, loss_weight=loss_weight) + self.initializer = initializer class CategoricalQHeadParameters(HeadParameters): diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index 63bda2bd5..7762abb61 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -321,7 +321,7 @@ def __init__(self, self.state_space = StateSpace({}) # observations - if not isinstance(self.env.observation_space, gym.spaces.dict.Dict): + if not isinstance(self.env.observation_space, gym.spaces.dict_space.Dict): state_space = {'observation': self.env.observation_space} else: state_space = self.env.observation_space.spaces From f16d6b2a802cbfb22acc4e6db7a917fa4ddd6485 Mon Sep 17 00:00:00 2001 From: Shadi Endrawis Date: Wed, 24 Apr 2019 18:27:35 +0300 Subject: [PATCH 07/11] SAC Documentation --- README.md | 1 + 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docs/contributing/add_env.html | 56 +- docs/dashboard.html | 58 +- docs/design/control_flow.html | 70 +- docs/design/horizontal_scaling.html | 66 +- docs/design/network.html | 69 +- docs/dist_usage.html | 62 +- docs/features/algorithms.html | 46 +- docs/features/benchmarks.html | 54 +- docs/features/environments.html | 78 +- docs/features/index.html | 46 +- docs/genindex.html | 89 +- docs/index.html | 52 +- docs/objects.inv | Bin 3579 -> 3541 bytes docs/search.html | 44 +- docs/searchindex.js | 2 +- docs/selecting_an_algorithm.html | 54 +- docs/test.html | 598 ++++++------- docs/usage.html | 62 +- .../source/_static/img/design_imgs/sac.png | Bin 0 -> 111523 bytes docs_raw/source/components/agents/index.rst | 1 + .../agents/policy_optimization/acer.rst | 1 + .../agents/policy_optimization/sac.rst | 50 ++ docs_raw/source/selecting_an_algorithm.rst | 8 + rl_coach/agents/soft_actor_critic_agent.py | 18 +- rl_coach/presets/Mujoco_SAC.py | 2 +- 166 files changed, 6498 insertions(+), 9470 deletions(-) create mode 100644 docs/_images/sac.png create mode 100644 docs/_modules/rl_coach/agents/agent_interface.html create mode 100644 docs/_modules/rl_coach/agents/soft_actor_critic_agent.html delete mode 100644 docs/_modules/rl_coach/environments/carla_environment.html delete mode 100644 docs/_modules/rl_coach/environments/control_suite_environment.html delete mode 100644 docs/_modules/rl_coach/environments/starcraft2_environment.html delete mode 100644 docs/_modules/rl_coach/orchestrators/kubernetes_orchestrator.html create mode 100644 docs/_sources/components/agents/policy_optimization/sac.rst.txt delete mode 100644 docs/_static/ajax-loader.gif delete mode 100644 docs/_static/comment-bright.png delete mode 100644 docs/_static/comment-close.png delete mode 100644 docs/_static/comment.png delete mode 100644 docs/_static/down-pressed.png delete mode 100644 docs/_static/down.png delete mode 100644 docs/_static/up-pressed.png delete mode 100644 docs/_static/up.png delete mode 100644 docs/_static/websupport.js create mode 100644 docs/components/agents/policy_optimization/sac.html create mode 100644 docs_raw/source/_static/img/design_imgs/sac.png create mode 100644 docs_raw/source/components/agents/policy_optimization/sac.rst diff --git a/README.md b/README.md index 9799ed5da..597bf81e9 100644 --- a/README.md +++ b/README.md @@ -277,6 +277,7 @@ dashboard * [Clipped Proximal Policy Optimization (CPPO)](https://arxiv.org/pdf/1707.06347.pdf) | **Multi Worker Single Node** ([code](rl_coach/agents/clipped_ppo_agent.py)) * [Generalized Advantage Estimation (GAE)](https://arxiv.org/abs/1506.02438) ([code](rl_coach/agents/actor_critic_agent.py#L86)) * [Sample Efficient Actor-Critic with Experience Replay (ACER)](https://arxiv.org/abs/1611.01224) | **Multi Worker Single Node** ([code](rl_coach/agents/acer_agent.py)) +* [Soft Actor-Critic (SAC)](https://arxiv.org/abs/1801.01290) | **Multi Worker Single Node** ([code](rl_coach/agents/soft_actor_critic_agent.py)) ### 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