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99 lines
4.3 KiB
Python
99 lines
4.3 KiB
Python
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Union
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import numpy as np
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from rl_coach.agents.agent import Agent
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from rl_coach.core_types import ActionInfo, StateType
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from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
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from rl_coach.spaces import DiscreteActionSpace
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## This is an abstract agent - there is no learn_from_batch method ##
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class ValueOptimizationAgent(Agent):
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def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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super().__init__(agent_parameters, parent)
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self.q_values = self.register_signal("Q")
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self.q_value_for_action = {}
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def init_environment_dependent_modules(self):
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super().init_environment_dependent_modules()
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if isinstance(self.spaces.action, DiscreteActionSpace):
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for i in range(len(self.spaces.action.actions)):
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self.q_value_for_action[i] = self.register_signal("Q for action {}".format(i),
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dump_one_value_per_episode=False,
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dump_one_value_per_step=True)
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# Algorithms for which q_values are calculated from predictions will override this function
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def get_all_q_values_for_states(self, states: StateType):
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if self.exploration_policy.requires_action_values():
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actions_q_values = self.get_prediction(states)
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else:
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actions_q_values = None
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return actions_q_values
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def get_prediction(self, states):
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return self.networks['main'].online_network.predict(self.prepare_batch_for_inference(states, 'main'))
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def update_transition_priorities_and_get_weights(self, TD_errors, batch):
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# update errors in prioritized replay buffer
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importance_weights = None
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if isinstance(self.memory, PrioritizedExperienceReplay):
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self.call_memory('update_priorities', (batch.info('idx'), TD_errors))
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importance_weights = batch.info('weight')
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return importance_weights
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def _validate_action(self, policy, action):
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if np.array(action).shape != ():
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raise ValueError((
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'The exploration_policy {} returned a vector of actions '
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'instead of a single action. ValueOptimizationAgents '
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'require exploration policies which return a single action.'
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).format(policy.__class__.__name__))
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def choose_action(self, curr_state):
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actions_q_values = self.get_all_q_values_for_states(curr_state)
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# choose action according to the exploration policy and the current phase (evaluating or training the agent)
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action = self.exploration_policy.get_action(actions_q_values)
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self._validate_action(self.exploration_policy, action)
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if actions_q_values is not None:
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# this is for bootstrapped dqn
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if type(actions_q_values) == list and len(actions_q_values) > 0:
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actions_q_values = self.exploration_policy.last_action_values
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actions_q_values = actions_q_values.squeeze()
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# store the q values statistics for logging
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self.q_values.add_sample(actions_q_values)
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for i, q_value in enumerate(actions_q_values):
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self.q_value_for_action[i].add_sample(q_value)
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action_info = ActionInfo(action=action,
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action_value=actions_q_values[action],
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max_action_value=np.max(actions_q_values))
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else:
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action_info = ActionInfo(action=action)
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return action_info
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def learn_from_batch(self, batch):
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raise NotImplementedError("ValueOptimizationAgent is an abstract agent. Not to be used directly.")
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