# rl_coach.agents.naf_agent — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.agents.naf_agent

    #
    # 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.
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    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

    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