coach v0.8.0

agents/dqn_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 *
+
+
+# Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
+class DQNAgent(ValueOptimizationAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
+        ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+
+    def learn_from_batch(self, batch):
+        current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
+
+        # for the action we actually took, the error is:
+        # TD error = r + discount*max(q_st_plus_1) - q_st
+        # for all other actions, the error is 0
+        q_st_plus_1 = self.main_network.target_network.predict(next_states)
+        # initialize with the current prediction so that we will
+        TD_targets = self.main_network.online_network.predict(current_states)
+
+        #  only update the action that we have actually done in this transition
+        for i in range(self.tp.batch_size):
+            TD_targets[i, actions[i]] = rewards[i] + (1.0 - game_overs[i]) * self.tp.agent.discount * np.max(
+                q_st_plus_1[i], 0)
+
+        result = self.main_network.train_and_sync_networks(current_states, TD_targets)
+        total_loss = result[0]
+
+        return total_loss