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

# Source code for rl_coach.agents.bc_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.
    #

    from typing import Union

    import numpy as np

    from rl_coach.agents.imitation_agent import ImitationAgent
    from rl_coach.architectures.head_parameters import PolicyHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, \
        MiddlewareScheme
    from rl_coach.exploration_policies.e_greedy import EGreedyParameters
    from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
    from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters

    class BCAlgorithmParameters(AlgorithmParameters):
        def __init__(self):
            super().__init__()

    class BCNetworkParameters(NetworkParameters):
        def __init__(self):
            super().__init__()
            self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
            self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
            self.heads_parameters = [PolicyHeadParameters()]
            self.optimizer_type = 'Adam'
            self.batch_size = 32
            self.replace_mse_with_huber_loss = False
            self.create_target_network = False

    class BCAgentParameters(AgentParameters):
        def __init__(self):
            super().__init__(algorithm=BCAlgorithmParameters(),
                             exploration=EGreedyParameters(),
                             memory=ExperienceReplayParameters(),
                             networks={"main": BCNetworkParameters()})

        @property
        def path(self):
            return 'rl_coach.agents.bc_agent:BCAgent'

    # Behavioral Cloning Agent
    class BCAgent(ImitationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)

        def learn_from_batch(self, batch):
            network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()

            # When using a policy head, the targets refer to the advantages that we are normally feeding the head with.
            # In this case, we need the policy head to just predict probabilities, so while we usually train the network
            # with log(Pi)*Advantages, in this specific case we will train it to log(Pi), which after the softmax will
            # predict Pi (=probabilities)
            targets = np.ones(batch.actions().shape[0])

            result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
                                                                    'output_0_0': batch.actions()},
                                                                   targets)
            total_loss, losses, unclipped_grads = result[:3]

            return total_loss, losses, unclipped_grads