coach v0.8.0

agents/value_optimization_agent.py

@@ -0,0 +1,64 @@
+#
+# 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.agent import *
+
+
+class ValueOptimizationAgent(Agent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=True):
+        Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+        self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
+                                           self.replicated_device, self.worker_device)
+        self.networks.append(self.main_network)
+        self.q_values = Signal("Q")
+        self.signals.append(self.q_values)
+
+    # Algorithms for which q_values are calculated from predictions will override this function
+    def get_q_values(self, prediction):
+        return prediction
+
+    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)
+        if self.tp.agent.use_measurements:
+            measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
+            prediction = self.main_network.online_network.predict([observation, measurements])
+        else:
+            prediction = self.main_network.online_network.predict(observation)
+
+        actions_q_values = self.get_q_values(prediction)
+
+        # 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)
+        else:
+            action = self.evaluation_exploration_policy.get_action(actions_q_values)
+
+        # this is for bootstrapped dqn
+        if type(actions_q_values) == list and len(actions_q_values) > 0:
+            actions_q_values = actions_q_values[self.exploration_policy.selected_head]
+        actions_q_values = actions_q_values.squeeze()
+
+        # 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