#
# 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.agent import Agent
from rl_coach.core_types import ActionInfo, StateType
from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
from rl_coach.spaces import DiscreteActionSpace
## 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.")