pre-release 0.10.0

rl_coach/agents/value_optimization_agent.py

@@ -0,0 +1,98 @@
+#
+# 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.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
+from rl_coach.spaces import DiscreteActionSpace
+
+from rl_coach.agents.agent import Agent
+from rl_coach.core_types import ActionInfo, StateType
+
+
+## This is an abstract agent - there is no learn_from_batch method ##
+
+
+class ValueOptimizationAgent(Agent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.q_values = self.register_signal("Q")
+        self.q_value_for_action = {}
+
+    def init_environment_dependent_modules(self):
+        super().init_environment_dependent_modules()
+        if isinstance(self.spaces.action, DiscreteActionSpace):
+            for i in range(len(self.spaces.action.actions)):
+                self.q_value_for_action[i] = self.register_signal("Q for action {}".format(i),
+                                                                  dump_one_value_per_episode=False,
+                                                                  dump_one_value_per_step=True)
+
+    # Algorithms for which q_values are calculated from predictions will override this function
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            actions_q_values = self.get_prediction(states)
+        else:
+            actions_q_values = None
+        return actions_q_values
+
+    def get_prediction(self, states):
+        return self.networks['main'].online_network.predict(self.prepare_batch_for_inference(states, 'main'))
+
+    def update_transition_priorities_and_get_weights(self, TD_errors, batch):
+        # update errors in prioritized replay buffer
+        importance_weights = None
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            self.call_memory('update_priorities', (batch.info('idx'), TD_errors))
+            importance_weights = batch.info('weight')
+        return importance_weights
+
+    def _validate_action(self, policy, action):
+        if np.array(action).shape != ():
+            raise ValueError((
+                'The exploration_policy {} returned a vector of actions '
+                'instead of a single action. ValueOptimizationAgents '
+                'require exploration policies which return a single action.'
+            ).format(policy.__class__.__name__))
+
+    def choose_action(self, curr_state):
+        actions_q_values = self.get_all_q_values_for_states(curr_state)
+
+        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
+        action = self.exploration_policy.get_action(actions_q_values)
+        self._validate_action(self.exploration_policy, action)
+
+        if actions_q_values is not None:
+            # this is for bootstrapped dqn
+            if type(actions_q_values) == list and len(actions_q_values) > 0:
+                actions_q_values = self.exploration_policy.last_action_values
+            actions_q_values = actions_q_values.squeeze()
+
+            # store the q values statistics for logging
+            self.q_values.add_sample(actions_q_values)
+            for i, q_value in enumerate(actions_q_values):
+                self.q_value_for_action[i].add_sample(q_value)
+
+            action_info = ActionInfo(action=action,
+                                     action_value=actions_q_values[action],
+                                     max_action_value=np.max(actions_q_values))
+        else:
+            action_info = ActionInfo(action=action)
+
+        return action_info
+
+    def learn_from_batch(self, batch):
+        raise NotImplementedError("ValueOptimizationAgent is an abstract agent. Not to be used directly.")