pre-release 0.10.0

rl_coach/agents/qr_dqn_agent.py

@@ -0,0 +1,112 @@
+#
+# 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.architectures.tensorflow_components.heads.quantile_regression_q_head import QuantileRegressionQHeadParameters
+from rl_coach.schedules import LinearSchedule
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.core_types import StateType
+
+
+class QuantileRegressionDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.heads_parameters = [QuantileRegressionQHeadParameters()]
+        self.learning_rate = 0.00005
+        self.optimizer_epsilon = 0.01 / 32
+
+
+class QuantileRegressionDQNAlgorithmParameters(DQNAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.atoms = 200
+        self.huber_loss_interval = 1  # called k in the paper
+
+
+class QuantileRegressionDQNAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = QuantileRegressionDQNAlgorithmParameters()
+        self.network_wrappers = {"main": QuantileRegressionDQNNetworkParameters()}
+        self.exploration.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+        self.exploration.evaluation_epsilon = 0.001
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.qr_dqn_agent:QuantileRegressionDQNAgent'
+
+
+# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
+class QuantileRegressionDQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.quantile_probabilities = np.ones(self.ap.algorithm.atoms) / float(self.ap.algorithm.atoms)
+
+    def get_q_values(self, quantile_values):
+        return np.dot(quantile_values, self.quantile_probabilities)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            quantile_values = self.get_prediction(states)
+            actions_q_values = self.get_q_values(quantile_values)
+        else:
+            actions_q_values = None
+        return actions_q_values
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the quantiles of the next states and current states
+        next_state_quantiles, current_quantiles = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        # get the optimal actions to take for the next states
+        target_actions = np.argmax(self.get_q_values(next_state_quantiles), axis=1)
+
+        # calculate the Bellman update
+        batch_idx = list(range(self.ap.network_wrappers['main'].batch_size))
+
+        TD_targets = batch.rewards(True) + (1.0 - batch.game_overs(True)) * self.ap.algorithm.discount \
+                               * next_state_quantiles[batch_idx, target_actions]
+
+        # get the locations of the selected actions within the batch for indexing purposes
+        actions_locations = [[b, a] for b, a in zip(batch_idx, batch.actions())]
+
+        # calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
+        cumulative_probabilities = np.array(range(self.ap.algorithm.atoms + 1)) / float(self.ap.algorithm.atoms) # tau_i
+        quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1])  # tau^hat_i
+        quantile_midpoints = np.tile(quantile_midpoints, (self.ap.network_wrappers['main'].batch_size, 1))
+        sorted_quantiles = np.argsort(current_quantiles[batch_idx, batch.actions()])
+        for idx in range(self.ap.network_wrappers['main'].batch_size):
+            quantile_midpoints[idx, :] = quantile_midpoints[idx, sorted_quantiles[idx]]
+
+        # train
+        result = self.networks['main'].train_and_sync_networks({
+            **batch.states(network_keys),
+            'output_0_0': actions_locations,
+            'output_0_1': quantile_midpoints,
+        }, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+