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

# Source code for rl_coach.agents.dqn_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.value_optimization_agent import ValueOptimizationAgent
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.architectures.head_parameters import QHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \
        MiddlewareScheme
    from rl_coach.core_types import EnvironmentSteps
    from rl_coach.exploration_policies.e_greedy import EGreedyParameters
    from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
    from rl_coach.schedules import LinearSchedule

    class DQNAlgorithmParameters(AlgorithmParameters):
        def __init__(self):
            super().__init__()
            self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
            self.num_consecutive_playing_steps = EnvironmentSteps(4)
            self.discount = 0.99
            self.supports_parameter_noise = True

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

    class DQNAgentParameters(AgentParameters):
        def __init__(self):
            super().__init__(algorithm=DQNAlgorithmParameters(),
                             exploration=EGreedyParameters(),
                             memory=ExperienceReplayParameters(),
                             networks={"main": DQNNetworkParameters()})
            self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000)
            self.exploration.evaluation_epsilon = 0.05

        @property
        def path(self):
            return 'rl_coach.agents.dqn_agent:DQNAgent'

    # Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
    class DQNAgent(ValueOptimizationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)

        def select_actions(self, next_states, q_st_plus_1):
            return np.argmax(q_st_plus_1, 1)

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

            # for the action we actually took, the error is:
            # TD error = r + discount*max(q_st_plus_1) - q_st
            # # for all other actions, the error is 0
            q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
                (self.networks['main'].target_network, batch.next_states(network_keys)),
                (self.networks['main'].online_network, batch.states(network_keys))
            ])

            selected_actions = self.select_actions(batch.next_states(network_keys), q_st_plus_1)

            # add Q value samples for logging
            self.q_values.add_sample(TD_targets)

            #  only update the action that we have actually done in this transition
            TD_errors = []
            for i in range(batch.size):
                new_target = batch.rewards()[i] +\
                             (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * q_st_plus_1[i][selected_actions[i]]
                TD_errors.append(np.abs(new_target - TD_targets[i, batch.actions()[i]]))
                TD_targets[i, batch.actions()[i]] = new_target

            # update errors in prioritized replay buffer
            importance_weights = self.update_transition_priorities_and_get_weights(TD_errors, batch)

            result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
                                                                   importance_weights=importance_weights)

            total_loss, losses, unclipped_grads = result[:3]

            return total_loss, losses, unclipped_grads