pre-release 0.10.0

rl_coach/agents/dqn_agent.py

@@ -0,0 +1,99 @@
+#
+# 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.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.tensorflow_components.heads.q_head import QHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \
+    InputEmbedderParameters, MiddlewareScheme
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+from rl_coach.schedules import LinearSchedule
+
+from rl_coach.core_types import EnvironmentSteps
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+
+
+class DQNAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
+        self.num_consecutive_playing_steps = EnvironmentSteps(4)
+        self.discount = 0.99
+
+
+class DQNNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [QHeadParameters()]
+        self.loss_weights = [1.0]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = True
+        self.create_target_network = True
+
+
+class DQNAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=DQNAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=ExperienceReplayParameters(),
+                         networks={"main": DQNNetworkParameters()})
+        self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000)
+        self.exploration.evaluation_epsilon = 0.05
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.dqn_agent:DQNAgent'
+
+
+# Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
+class DQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # 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, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        #  only update the action that we have actually done in this transition
+        TD_errors = []
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            new_target = batch.rewards()[i] +\
+                         (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * np.max(q_st_plus_1[i], 0)
+            TD_errors.append(np.abs(new_target - TD_targets[i, batch.actions()[i]]))
+            TD_targets[i, batch.actions()[i]] = new_target
+
+        # update errors in prioritized replay buffer
+        importance_weights = self.update_transition_priorities_and_get_weights(TD_errors, batch)
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads