pre-release 0.10.0

rl_coach/agents/categorical_dqn_agent.py

@@ -0,0 +1,114 @@
+#
+# 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 DQNNetworkParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.tensorflow_components.heads.categorical_q_head import CategoricalQHeadParameters
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+from rl_coach.schedules import LinearSchedule
+
+from rl_coach.core_types import StateType
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+
+
+class CategoricalDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.heads_parameters = [CategoricalQHeadParameters()]
+
+
+class CategoricalDQNAlgorithmParameters(DQNAlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.v_min = -10.0
+        self.v_max = 10.0
+        self.atoms = 51
+
+
+class CategoricalDQNExplorationParameters(EGreedyParameters):
+    def __init__(self):
+        super().__init__()
+        self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+        self.evaluation_epsilon = 0.001
+
+
+class CategoricalDQNAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=CategoricalDQNAlgorithmParameters(),
+                         exploration=CategoricalDQNExplorationParameters(),
+                         memory=ExperienceReplayParameters(),
+                         networks={"main": CategoricalDQNNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent'
+
+
+# Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
+class CategoricalDQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.z_values = np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max, self.ap.algorithm.atoms)
+
+    def distribution_prediction_to_q_values(self, prediction):
+        return np.dot(prediction, self.z_values)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            prediction = self.get_prediction(states)
+            q_values = self.distribution_prediction_to_q_values(prediction)
+        else:
+            q_values = None
+        return q_values
+
+    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 calculated by the atoms distribution
+        # for all other actions, the error is 0
+        distributed_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
+        target_actions = np.argmax(self.distribution_prediction_to_q_values(distributed_q_st_plus_1), axis=1)
+        m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
+
+        batches = np.arange(self.ap.network_wrappers['main'].batch_size)
+        for j in range(self.z_values.size):
+            tzj = np.fmax(np.fmin(batch.rewards() +
+                                  (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
+                                  self.z_values[self.z_values.size - 1]),
+                          self.z_values[0])
+            bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
+            u = (np.ceil(bj)).astype(int)
+            l = (np.floor(bj)).astype(int)
+            m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
+            m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
+        # total_loss = cross entropy between actual result above and predicted result for the given action
+        TD_targets[batches, batch.actions()] = m
+
+        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
+