pre-release 0.10.0

rl_coach/agents/pal_agent.py

@@ -0,0 +1,94 @@
+#
+# 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, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay, \
+    EpisodicExperienceReplayParameters
+
+
+class PALAlgorithmParameters(DQNAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.pal_alpha = 0.9
+        self.persistent_advantage_learning = False
+        self.monte_carlo_mixing_rate = 0.1
+
+
+class PALAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = PALAlgorithmParameters()
+        self.memory = EpisodicExperienceReplayParameters()
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.pal_agent:PALAgent'
+
+
+# Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf
+class PALAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.alpha = agent_parameters.algorithm.pal_alpha
+        self.persistent = agent_parameters.algorithm.persistent_advantage_learning
+        self.monte_carlo_mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # next state values
+        q_st_plus_1_target, q_st_plus_1_online = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.next_states(network_keys))
+        ])
+        selected_actions = np.argmax(q_st_plus_1_online, 1)
+        v_st_plus_1_target = np.max(q_st_plus_1_target, 1)
+
+        # current state values
+        q_st_target, q_st_online = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+        v_st_target = np.max(q_st_target, 1)
+
+        # calculate TD error
+        TD_targets = np.copy(q_st_online)
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \
+                                        (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
+                                                     q_st_plus_1_target[i][selected_actions[i]]
+            advantage_learning_update = v_st_target[i] - q_st_target[i, batch.actions()[i]]
+            next_advantage_learning_update = v_st_plus_1_target[i] - q_st_plus_1_target[i, selected_actions[i]]
+            # Persistent Advantage Learning or Regular Advantage Learning
+            if self.persistent:
+                TD_targets[i, batch.actions()[i]] -= self.alpha * min(advantage_learning_update, next_advantage_learning_update)
+            else:
+                TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update
+
+            # mixing monte carlo updates
+            monte_carlo_target = batch.total_returns()[i]
+            TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \
+                                        + self.monte_carlo_mixing_rate * monte_carlo_target
+
+        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