#
# 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.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.head_parameters import NAFHeadParameters
from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, \
NetworkParameters
from rl_coach.core_types import ActionInfo, EnvironmentSteps
from rl_coach.exploration_policies.ou_process import OUProcessParameters
from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
from rl_coach.spaces import BoxActionSpace
class NAFNetworkParameters(NetworkParameters):
def __init__(self):
super().__init__()
self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
self.middleware_parameters = FCMiddlewareParameters()
self.heads_parameters = [NAFHeadParameters()]
self.optimizer_type = 'Adam'
self.learning_rate = 0.001
self.async_training = True
self.create_target_network = True
[docs]class NAFAlgorithmParameters(AlgorithmParameters):
def __init__(self):
super().__init__()
self.num_consecutive_training_steps = 5
self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
self.rate_for_copying_weights_to_target = 0.001
class NAFAgentParameters(AgentParameters):
def __init__(self):
super().__init__(algorithm=NAFAlgorithmParameters(),
exploration=OUProcessParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={"main": NAFNetworkParameters()})
@property
def path(self):
return 'rl_coach.agents.naf_agent:NAFAgent'
# Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf
class NAFAgent(ValueOptimizationAgent):
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
super().__init__(agent_parameters, parent)
self.l_values = self.register_signal("L")
self.a_values = self.register_signal("Advantage")
self.mu_values = self.register_signal("Action")
self.v_values = self.register_signal("V")
self.TD_targets = self.register_signal("TD targets")
def learn_from_batch(self, batch):
network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
# TD error = r + discount*v_st_plus_1 - q_st
v_st_plus_1 = self.networks['main'].target_network.predict(
batch.next_states(network_keys),
self.networks['main'].target_network.output_heads[0].V,
squeeze_output=False,
)
TD_targets = np.expand_dims(batch.rewards(), -1) + \
(1.0 - np.expand_dims(batch.game_overs(), -1)) * self.ap.algorithm.discount * v_st_plus_1
self.TD_targets.add_sample(TD_targets)
result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
'output_0_0': batch.actions(len(batch.actions().shape) == 1)
}, TD_targets)
total_loss, losses, unclipped_grads = result[:3]
return total_loss, losses, unclipped_grads
def choose_action(self, curr_state):
if type(self.spaces.action) != BoxActionSpace:
raise ValueError('NAF works only for continuous control problems')
# convert to batch so we can run it through the network
tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
naf_head = self.networks['main'].online_network.output_heads[0]
action_values = self.networks['main'].online_network.predict(tf_input_state, outputs=naf_head.mu,
squeeze_output=False)
# get the actual action to use
action = self.exploration_policy.get_action(action_values)
# get the internal values for logging
outputs = [naf_head.mu, naf_head.Q, naf_head.L, naf_head.A, naf_head.V]
result = self.networks['main'].online_network.predict(
{**tf_input_state, 'output_0_0': action_values},
outputs=outputs
)
mu, Q, L, A, V = result
# store the q values statistics for logging
self.q_values.add_sample(Q)
self.l_values.add_sample(L)
self.a_values.add_sample(A)
self.mu_values.add_sample(mu)
self.v_values.add_sample(V)
action_info = ActionInfo(action=action, action_value=Q)
return action_info