coach v0.8.0

agents/ddpg_agent.py

@@ -0,0 +1,104 @@
+#
+# 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 agents.actor_critic_agent import *
+from configurations import *
+
+
+# Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf
+class DDPGAgent(ActorCriticAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
+        ActorCriticAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
+                                  create_target_network=True)
+        # define critic network
+        self.critic_network = self.main_network
+        # self.networks.append(self.critic_network)
+
+        # define actor network
+        tuning_parameters.agent.input_types = [InputTypes.Observation]
+        tuning_parameters.agent.output_types = [OutputTypes.Pi]
+        self.actor_network = NetworkWrapper(tuning_parameters, True, self.has_global, 'actor',
+                                            self.replicated_device, self.worker_device)
+        self.networks.append(self.actor_network)
+
+        self.q_values = Signal("Q")
+        self.signals.append(self.q_values)
+
+    def learn_from_batch(self, batch):
+        current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
+
+        # TD error = r + discount*max(q_st_plus_1) - q_st
+        next_actions = self.actor_network.target_network.predict([next_states])
+        q_st_plus_1 = self.critic_network.target_network.predict([next_states, next_actions])
+        TD_targets = np.expand_dims(rewards, -1) + \
+                     (1.0 - np.expand_dims(game_overs, -1)) * self.tp.agent.discount * q_st_plus_1
+
+        # get the gradients of the critic output with respect to the action
+        actions_mean = self.actor_network.online_network.predict(current_states)
+        critic_online_network = self.critic_network.online_network
+        action_gradients = self.critic_network.sess.run(critic_online_network.gradients_wrt_inputs[1],
+                                                        feed_dict={
+                                                            critic_online_network.inputs[0]: current_states,
+                                                            critic_online_network.inputs[1]: actions_mean,
+                                                        })[0]
+
+        # train the critic
+        if len(actions.shape) == 1:
+            actions = np.expand_dims(actions, -1)
+        result = self.critic_network.train_and_sync_networks([current_states, actions], TD_targets)
+        total_loss = result[0]
+
+        # apply the gradients from the critic to the actor
+        actor_online_network = self.actor_network.online_network
+        gradients = self.actor_network.sess.run(actor_online_network.weighted_gradients,
+                                                feed_dict={
+                                                    actor_online_network.gradients_weights_ph: -action_gradients,
+                                                    actor_online_network.inputs[0]: current_states
+                                                })
+        if self.actor_network.has_global:
+            self.actor_network.global_network.apply_gradients(gradients)
+            self.actor_network.update_online_network()
+        else:
+            self.actor_network.online_network.apply_gradients(gradients)
+
+        return total_loss
+
+    def train(self):
+        return Agent.train(self)
+
+    def choose_action(self, curr_state, phase=RunPhase.TRAIN):
+        assert not self.env.discrete_controls, 'DDPG works only for continuous control problems'
+        # convert to batch so we can run it through the network
+        observation = np.expand_dims(np.array(curr_state['observation']), 0)
+        result = self.actor_network.online_network.predict(observation)
+        action_values = result[0].squeeze()
+
+        if phase == RunPhase.TRAIN:
+            action = self.exploration_policy.get_action(action_values)
+        else:
+            action = action_values
+
+        action = np.clip(action, self.env.action_space_low, self.env.action_space_high)
+
+        # get q value
+        action_batch = np.expand_dims(action, 0)
+        if type(action) != np.ndarray:
+            action_batch = np.array([[action]])
+        q_value = self.critic_network.online_network.predict([observation, action_batch])[0]
+        self.q_values.add_sample(q_value)
+        action_info = {"action_value": q_value}
+
+        return action, action_info