pre-release 0.10.0

rl_coach/agents/n_step_q_agent.py

@@ -0,0 +1,126 @@
+#
+# 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.policy_optimization_agent import PolicyOptimizationAgent
+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, AgentParameters, NetworkParameters, \
+    InputEmbedderParameters
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.utils import last_sample
+
+from rl_coach.core_types import EnvironmentSteps
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+
+
+class NStepQNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [QHeadParameters()]
+        self.loss_weights = [1.0]
+        self.optimizer_type = 'Adam'
+        self.async_training = True
+        self.shared_optimizer = True
+        self.create_target_network = True
+
+
+class NStepQAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
+        self.apply_gradients_every_x_episodes = 1
+        self.num_steps_between_gradient_updates = 5  # this is called t_max in all the papers
+        self.targets_horizon = 'N-Step'
+
+
+class NStepQAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NStepQAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": NStepQNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.n_step_q_agent:NStepQAgent'
+
+
+# N Step Q Learning Agent - https://arxiv.org/abs/1602.01783
+class NStepQAgent(ValueOptimizationAgent, PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.last_gradient_update_step_idx = 0
+        self.q_values = self.register_signal('Q Values')
+        self.value_loss = self.register_signal('Value Loss')
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the values for the current states
+        state_value_head_targets = self.networks['main'].online_network.predict(batch.states(network_keys))
+
+        # the targets for the state value estimator
+        if self.ap.algorithm.targets_horizon == '1-Step':
+            # 1-Step Q learning
+            q_st_plus_1 = self.networks['main'].target_network.predict(batch.next_states(network_keys))
+
+            for i in reversed(range(batch.size)):
+                state_value_head_targets[i][batch.actions()[i]] = \
+                    batch.rewards()[i] \
+                    + (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * np.max(q_st_plus_1[i], 0)
+
+        elif self.ap.algorithm.targets_horizon == 'N-Step':
+            # N-Step Q learning
+            if batch.game_overs()[-1]:
+                R = 0
+            else:
+                R = np.max(self.networks['main'].target_network.predict(last_sample(batch.next_states(network_keys))))
+
+            for i in reversed(range(batch.size)):
+                R = batch.rewards()[i] + self.ap.algorithm.discount * R
+                state_value_head_targets[i][batch.actions()[i]] = R
+
+        else:
+            assert True, 'The available values for targets_horizon are: 1-Step, N-Step'
+
+        # train
+        result = self.networks['main'].online_network.accumulate_gradients(batch.states(network_keys), [state_value_head_targets])
+
+        # logging
+        total_loss, losses, unclipped_grads = result[:3]
+        self.value_loss.add_sample(losses[0])
+
+        return total_loss, losses, unclipped_grads
+
+    def train(self):
+        # update the target network of every network that has a target network
+        if any([network.has_target for network in self.networks.values()]) \
+                and self._should_update_online_weights_to_target():
+            for network in self.networks.values():
+                network.update_target_network(self.ap.algorithm.rate_for_copying_weights_to_target)
+
+            self.agent_logger.create_signal_value('Update Target Network', 1)
+        else:
+            self.agent_logger.create_signal_value('Update Target Network', 0, overwrite=False)
+
+        return PolicyOptimizationAgent.train(self)