coach v0.8.0

agents/nec_agent.py

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+#
+# 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.value_optimization_agent import *
+
+
+# Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf
+class NECAgent(ValueOptimizationAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
+        ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
+                                        create_target_network=False)
+        self.current_episode_state_embeddings = []
+        self.current_episode_actions = []
+        self.training_started = False
+
+    def learn_from_batch(self, batch):
+        if not self.main_network.online_network.output_heads[0].DND.has_enough_entries(self.tp.agent.number_of_knn):
+            return 0
+        else:
+            if not self.training_started:
+                self.training_started = True
+                screen.log_title("Finished collecting initial entries in DND. Starting to train network...")
+
+        current_states, next_states, actions, rewards, game_overs, total_return = self.extract_batch(batch)
+        result = self.main_network.train_and_sync_networks([current_states, actions], total_return)
+        total_loss = result[0]
+
+        return total_loss
+
+    def choose_action(self, curr_state, phase=RunPhase.TRAIN):
+        # convert to batch so we can run it through the network
+        observation = np.expand_dims(np.array(curr_state['observation']), 0)
+
+        # get embedding
+        embedding = self.main_network.sess.run(self.main_network.online_network.state_embedding,
+                                               feed_dict={self.main_network.online_network.inputs[0]: observation})
+        self.current_episode_state_embeddings.append(embedding[0])
+
+        # get action values
+        if self.main_network.online_network.output_heads[0].DND.has_enough_entries(self.tp.agent.number_of_knn):
+            # if there are enough entries in the DND then we can query it to get the action values
+            actions_q_values = []
+            for action in range(self.action_space_size):
+                feed_dict = {
+                    self.main_network.online_network.state_embedding: embedding,
+                    self.main_network.online_network.output_heads[0].input[0]: np.array([action])
+                }
+                q_value = self.main_network.sess.run(
+                    self.main_network.online_network.output_heads[0].output, feed_dict=feed_dict)
+                actions_q_values.append(q_value[0])
+        else:
+            # get only the embedding so we can insert it to the DND
+            actions_q_values = [0] * self.action_space_size
+
+        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
+        if phase == RunPhase.TRAIN:
+            action = self.exploration_policy.get_action(actions_q_values)
+            self.current_episode_actions.append(action)
+        else:
+            action = np.argmax(actions_q_values)
+
+        # store the q values statistics for logging
+        self.q_values.add_sample(actions_q_values)
+
+        # store information for plotting interactively (actual plotting is done in agent)
+        if self.tp.visualization.plot_action_values_online:
+            for idx, action_name in enumerate(self.env.actions_description):
+                self.episode_running_info[action_name].append(actions_q_values[idx])
+
+        action_value = {"action_value": actions_q_values[action]}
+        return action, action_value
+
+    def reset_game(self, do_not_reset_env=False):
+        ValueOptimizationAgent.reset_game(self, do_not_reset_env)
+
+        # make sure we already have at least one episode
+        if self.memory.num_complete_episodes() >= 1 and not self.in_heatup:
+            # get the last full episode that we have collected
+            episode = self.memory.get(-2)
+            returns = []
+            for i in range(episode.length()):
+                returns.append(episode.get_transition(i).total_return)
+            # Just to deal with the end of heatup where there might be a case where it ends in a middle
+            # of an episode, and thus when getting the episode out of the ER, it will be a complete one whereas
+            # the other statistics collected here, are collected only during training.
+            returns = returns[-len(self.current_episode_actions):]
+            self.main_network.online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
+                                                                     self.current_episode_actions, returns)
+
+        self.current_episode_state_embeddings = []
+        self.current_episode_actions = []