pre-release 0.10.0

rl_coach/agents/bootstrapped_dqn_agent.py

@@ -0,0 +1,84 @@
+#
+# 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 typing import Union
+
+import numpy as np
+from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+
+from rl_coach.exploration_policies.bootstrapped import BootstrappedParameters
+
+
+class BootstrappedDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_output_head_copies = 10
+        self.rescale_gradient_from_head_by_factor = [1.0/self.num_output_head_copies]*self.num_output_head_copies
+
+
+class BootstrappedDQNAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.network_wrappers = {"main": BootstrappedDQNNetworkParameters()}
+        self.exploration = BootstrappedParameters()
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.bootstrapped_dqn_agent:BootstrappedDQNAgent'
+
+
+# Bootstrapped DQN - https://arxiv.org/pdf/1602.04621.pdf
+class BootstrappedDQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def reset_internal_state(self):
+        super().reset_internal_state()
+        self.exploration_policy.select_head()
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        next_states_online_values = self.networks['main'].online_network.predict(batch.next_states(network_keys))
+        result = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+        q_st_plus_1 = result[:self.ap.exploration.architecture_num_q_heads]
+        TD_targets = result[self.ap.exploration.architecture_num_q_heads:]
+
+        # initialize with the current prediction so that we will
+        #  only update the action that we have actually done in this transition
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            mask = batch[i].info['mask']
+            for head_idx in range(self.ap.exploration.architecture_num_q_heads):
+                if mask[head_idx] == 1:
+                    selected_action = np.argmax(next_states_online_values[head_idx][i], 0)
+                    TD_targets[head_idx][i, batch.actions()[i]] = \
+                        batch.rewards()[i] + (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount \
+                                     * q_st_plus_1[head_idx][i][selected_action]
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+    def observe(self, env_response):
+        mask = np.random.binomial(1, self.ap.exploration.bootstrapped_data_sharing_probability,
+                                  self.ap.exploration.architecture_num_q_heads)
+        env_response.info['mask'] = mask
+        return super().observe(env_response)