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

# Source code for rl_coach.agents.cil_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

    from rl_coach.agents.imitation_agent import ImitationAgent
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.architectures.head_parameters import RegressionHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters
    from rl_coach.exploration_policies.e_greedy import EGreedyParameters
    from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters

    class CILAlgorithmParameters(AlgorithmParameters):
        """
        :param state_key_with_the_class_index: (str)
            The key of the state dictionary which corresponds to the value that will be used to control the class index.
        """
        def __init__(self):
            super().__init__()
            self.state_key_with_the_class_index = 'high_level_command'

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

    class CILAgentParameters(AgentParameters):
        def __init__(self):
            super().__init__(algorithm=CILAlgorithmParameters(),
                             exploration=EGreedyParameters(),
                             memory=BalancedExperienceReplayParameters(),
                             networks={"main": CILNetworkParameters()})

        @property
        def path(self):
            return 'rl_coach.agents.cil_agent:CILAgent'

    # Conditional Imitation Learning Agent: https://arxiv.org/abs/1710.02410
    class CILAgent(ImitationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            self.current_high_level_control = 0

        def choose_action(self, curr_state):
            self.current_high_level_control = curr_state[self.ap.algorithm.state_key_with_the_class_index]
            return super().choose_action(curr_state)

        def extract_action_values(self, prediction):
            return prediction[self.current_high_level_control].squeeze()

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

            target_values = self.networks['main'].online_network.predict({**batch.states(network_keys)})

            branch_to_update = batch.states([self.ap.algorithm.state_key_with_the_class_index])[self.ap.algorithm.state_key_with_the_class_index]
            for idx, branch in enumerate(branch_to_update):
                target_values[branch][idx] = batch.actions()[idx]

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

            return total_loss, losses, unclipped_grads