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110 lines
5.0 KiB
Python
110 lines
5.0 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from agents.actor_critic_agent import *
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from configurations import *
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# Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf
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class DDPGAgent(ActorCriticAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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ActorCriticAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
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create_target_network=True)
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# define critic network
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self.critic_network = self.main_network
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# self.networks.append(self.critic_network)
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# define actor network
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tuning_parameters.agent.input_types = {'observation': InputTypes.Observation}
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tuning_parameters.agent.output_types = [OutputTypes.Pi]
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self.actor_network = NetworkWrapper(tuning_parameters, True, self.has_global, 'actor',
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self.replicated_device, self.worker_device)
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self.networks.append(self.actor_network)
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self.q_values = Signal("Q")
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self.signals.append(self.q_values)
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self.reset_game(do_not_reset_env=True)
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def learn_from_batch(self, batch):
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current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
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# TD error = r + discount*max(q_st_plus_1) - q_st
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next_actions = self.actor_network.target_network.predict(next_states)
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inputs = copy.copy(next_states)
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inputs['action'] = next_actions
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q_st_plus_1 = self.critic_network.target_network.predict(inputs)
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TD_targets = np.expand_dims(rewards, -1) + \
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(1.0 - np.expand_dims(game_overs, -1)) * self.tp.agent.discount * q_st_plus_1
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# get the gradients of the critic output with respect to the action
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actions_mean = self.actor_network.online_network.predict(current_states)
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critic_online_network = self.critic_network.online_network
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# TODO: convert into call to predict, current method ignores lstm middleware for example
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action_gradients = self.critic_network.sess.run(critic_online_network.gradients_wrt_inputs['action'],
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feed_dict=critic_online_network._feed_dict({
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**current_states,
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'action': actions_mean,
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}))[0]
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# train the critic
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if len(actions.shape) == 1:
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actions = np.expand_dims(actions, -1)
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result = self.critic_network.train_and_sync_networks({**current_states, 'action': actions}, TD_targets)
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total_loss = result[0]
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# apply the gradients from the critic to the actor
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actor_online_network = self.actor_network.online_network
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gradients = self.actor_network.sess.run(actor_online_network.weighted_gradients,
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feed_dict=actor_online_network._feed_dict({
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**current_states,
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actor_online_network.gradients_weights_ph: -action_gradients,
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}))
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if self.actor_network.has_global:
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self.actor_network.global_network.apply_gradients(gradients)
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self.actor_network.update_online_network()
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else:
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self.actor_network.online_network.apply_gradients(gradients)
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return total_loss
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def train(self):
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return Agent.train(self)
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def choose_action(self, curr_state, phase=RunPhase.TRAIN):
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assert not self.env.discrete_controls, 'DDPG works only for continuous control problems'
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result = self.actor_network.online_network.predict(self.tf_input_state(curr_state))
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action_values = result[0].squeeze()
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if phase == RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = action_values
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action = np.clip(action, self.env.action_space_low, self.env.action_space_high)
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# get q value
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action_batch = np.expand_dims(action, 0)
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if type(action) != np.ndarray:
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action_batch = np.array([[action]])
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inputs = self.tf_input_state(curr_state)
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inputs['action'] = action_batch
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q_value = self.critic_network.online_network.predict(inputs)[0]
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self.q_values.add_sample(q_value)
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action_info = {"action_value": q_value}
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return action, action_info
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