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

# Source code for rl_coach.agents.categorical_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.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
    from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
    from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
    from rl_coach.core_types import StateType
    from rl_coach.exploration_policies.e_greedy import EGreedyParameters
    from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
    from rl_coach.schedules import LinearSchedule

    class CategoricalDQNNetworkParameters(DQNNetworkParameters):
        def __init__(self):
            super().__init__()
            self.heads_parameters = [CategoricalQHeadParameters()]

    class CategoricalDQNAlgorithmParameters(DQNAlgorithmParameters):
        """
        :param v_min: (float)
            The minimal value that will be represented in the network output for predicting the Q value.
            Corresponds to :math:`v_{min}` in the paper.

        :param v_max: (float)
            The maximum value that will be represented in the network output for predicting the Q value.
            Corresponds to :math:`v_{max}` in the paper.

        :param atoms: (int)
            The number of atoms that will be used to discretize the range between v_min and v_max.
            For the C51 algorithm described in the paper, the number of atoms is 51.
        """
        def __init__(self):
            super().__init__()
            self.v_min = -10.0
            self.v_max = 10.0
            self.atoms = 51

    class CategoricalDQNExplorationParameters(EGreedyParameters):
        def __init__(self):
            super().__init__()
            self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
            self.evaluation_epsilon = 0.001

    class CategoricalDQNAgentParameters(DQNAgentParameters):
        def __init__(self):
            super().__init__()
            self.algorithm = CategoricalDQNAlgorithmParameters()
            self.exploration = CategoricalDQNExplorationParameters()
            self.network_wrappers = {"main": CategoricalDQNNetworkParameters()}

        @property
        def path(self):
            return 'rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent'

    # Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
    class CategoricalDQNAgent(ValueOptimizationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            self.z_values = np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max, self.ap.algorithm.atoms)

        def distribution_prediction_to_q_values(self, prediction):
            return np.dot(prediction, self.z_values)

        # prediction's format is (batch,actions,atoms)
        def get_all_q_values_for_states(self, states: StateType):
            if self.exploration_policy.requires_action_values():
                q_values = self.get_prediction(states,
                                               outputs=[self.networks['main'].online_network.output_heads[0].q_values])
            else:
                q_values = None
            return q_values

        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 calculated by the atoms distribution
            # for all other actions, the error is 0
            distributional_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))
            ])

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

            # select the optimal actions for the next state
            target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1)
            m = np.zeros((batch.size, self.z_values.size))

            batches = np.arange(batch.size)

            # an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping.
            # only 10% speedup overall - leaving commented out as the code is not as clear.

            # tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount *
            #                        np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)),
            #                     self.z_values[-1]),
            #             self.z_values[0])
            #
            # bj_ = (tzj_ - self.z_values[0]) / (self.z_values[1] - self.z_values[0])
            # u_ = (np.ceil(bj_)).astype(int)
            # l_ = (np.floor(bj_)).astype(int)
            # m_ = np.zeros((batch.size, self.z_values.size))
            # np.add.at(m_, [batches, l_],
            #           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_))
            # np.add.at(m_, [batches, u_],
            #           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_))

            for j in range(self.z_values.size):
                tzj = np.fmax(np.fmin(batch.rewards() +
                                      (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
                                      self.z_values[-1]),
                              self.z_values[0])
                bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
                u = (np.ceil(bj)).astype(int)
                l = (np.floor(bj)).astype(int)
                m[batches, l] += (distributional_q_st_plus_1[batches, target_actions, j] * (u - bj))
                m[batches, u] += (distributional_q_st_plus_1[batches, target_actions, j] * (bj - l))

            # total_loss = cross entropy between actual result above and predicted result for the given action
            # only update the action that we have actually done in this transition
            TD_targets[batches, batch.actions()] = m

            # update errors in prioritized replay buffer
            importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None

            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]

            # TODO: fix this spaghetti code
            if isinstance(self.memory, PrioritizedExperienceReplay):
                errors = losses[0][np.arange(batch.size), batch.actions()]
                self.call_memory('update_priorities', (batch.info('idx'), errors))

            return total_loss, losses, unclipped_grads