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

# Source code for rl_coach.agents.actor_critic_agent

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    # Copyright (c) 2017 Intel Corporation
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    # 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
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    #      http://www.apache.org/licenses/LICENSE-2.0
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    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
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    from typing import Union

    import numpy as np
    import scipy.signal

    from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
    from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
    from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters
    from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
    from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
        AgentParameters
    from rl_coach.exploration_policies.categorical import CategoricalParameters
    from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
    from rl_coach.logger import screen
    from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
    from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
    from rl_coach.utils import last_sample

    class ActorCriticAlgorithmParameters(AlgorithmParameters):
        """
        :param policy_gradient_rescaler: (PolicyGradientRescaler)
            The value that will be used to rescale the policy gradient

        :param apply_gradients_every_x_episodes: (int)
            The number of episodes to wait before applying the accumulated gradients to the network.
            The training iterations only accumulate gradients without actually applying them.

        :param beta_entropy: (float)
            The weight that will be given to the entropy regularization which is used in order to improve exploration.

        :param num_steps_between_gradient_updates: (int)
            Every num_steps_between_gradient_updates transitions will be considered as a single batch and use for
            accumulating gradients. This is also the number of steps used for bootstrapping according to the n-step formulation.

        :param gae_lambda: (float)
            If the policy gradient rescaler was defined as PolicyGradientRescaler.GAE, the generalized advantage estimation
            scheme will be used, in which case the lambda value controls the decay for the different n-step lengths.

        :param estimate_state_value_using_gae: (bool)
            If set to True, the state value targets for the V head will be estimated using the GAE scheme.
        """
        def __init__(self):
            super().__init__()
            self.policy_gradient_rescaler = PolicyGradientRescaler.A_VALUE
            self.apply_gradients_every_x_episodes = 5
            self.beta_entropy = 0
            self.num_steps_between_gradient_updates = 5000  # this is called t_max in all the papers
            self.gae_lambda = 0.96
            self.estimate_state_value_using_gae = False

    class ActorCriticNetworkParameters(NetworkParameters):
        def __init__(self):
            super().__init__()
            self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
            self.middleware_parameters = FCMiddlewareParameters()
            self.heads_parameters = [VHeadParameters(loss_weight=0.5), PolicyHeadParameters(loss_weight=1.0)]
            self.optimizer_type = 'Adam'
            self.clip_gradients = 40.0
            self.async_training = True

    class ActorCriticAgentParameters(AgentParameters):
        def __init__(self):
            super().__init__(algorithm=ActorCriticAlgorithmParameters(),
                             exploration={DiscreteActionSpace: CategoricalParameters(),
                                          BoxActionSpace: ContinuousEntropyParameters()},
                             memory=SingleEpisodeBufferParameters(),
                             networks={"main": ActorCriticNetworkParameters()})

        @property
        def path(self):
            return 'rl_coach.agents.actor_critic_agent:ActorCriticAgent'

    # Actor Critic - https://arxiv.org/abs/1602.01783
    class ActorCriticAgent(PolicyOptimizationAgent):
        def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
            super().__init__(agent_parameters, parent)
            self.last_gradient_update_step_idx = 0
            self.action_advantages = self.register_signal('Advantages')
            self.state_values = self.register_signal('Values')
            self.value_loss = self.register_signal('Value Loss')
            self.policy_loss = self.register_signal('Policy Loss')

        # Discounting function used to calculate discounted returns.
        def discount(self, x, gamma):
            return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]

        def get_general_advantage_estimation_values(self, rewards, values):
            # values contain n+1 elements (t ... t+n+1), rewards contain n elements (t ... t + n)
            bootstrap_extended_rewards = np.array(rewards.tolist() + [values[-1]])

            # Approximation based calculation of GAE (mathematically correct only when Tmax = inf,
            # although in practice works even in much smaller Tmax values, e.g. 20)
            deltas = rewards + self.ap.algorithm.discount * values[1:] - values[:-1]
            gae = self.discount(deltas, self.ap.algorithm.discount * self.ap.algorithm.gae_lambda)

            if self.ap.algorithm.estimate_state_value_using_gae:
                discounted_returns = np.expand_dims(gae + values[:-1], -1)
            else:
                discounted_returns = np.expand_dims(np.array(self.discount(bootstrap_extended_rewards,
                                                                           self.ap.algorithm.discount)), 1)[:-1]
            return gae, discounted_returns

        def learn_from_batch(self, batch):
            # batch contains a list of episodes to learn from
            network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()

            # get the values for the current states

            result = self.networks['main'].online_network.predict(batch.states(network_keys))
            current_state_values = result[0]

            self.state_values.add_sample(current_state_values)

            # the targets for the state value estimator
            num_transitions = batch.size
            state_value_head_targets = np.zeros((num_transitions, 1))

            # estimate the advantage function
            action_advantages = np.zeros((num_transitions, 1))

            if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
                if batch.game_overs()[-1]:
                    R = 0
                else:
                    R = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]

                for i in reversed(range(num_transitions)):
                    R = batch.rewards()[i] + self.ap.algorithm.discount * R
                    state_value_head_targets[i] = R
                    action_advantages[i] = R - current_state_values[i]

            elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
                # get bootstraps
                bootstrapped_value = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]
                values = np.append(current_state_values, bootstrapped_value)
                if batch.game_overs()[-1]:
                    values[-1] = 0

                # get general discounted returns table
                gae_values, state_value_head_targets = self.get_general_advantage_estimation_values(batch.rewards(), values)
                action_advantages = np.vstack(gae_values)
            else:
                screen.warning("WARNING: The requested policy gradient rescaler is not available")

            action_advantages = action_advantages.squeeze(axis=-1)
            actions = batch.actions()
            if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) < 2:
                actions = np.expand_dims(actions, -1)

            # train
            result = self.networks['main'].online_network.accumulate_gradients({**batch.states(network_keys),
                                                                                'output_1_0': actions},
                                                                           [state_value_head_targets, action_advantages])

            # logging
            total_loss, losses, unclipped_grads = result[:3]
            self.action_advantages.add_sample(action_advantages)
            self.unclipped_grads.add_sample(unclipped_grads)
            self.value_loss.add_sample(losses[0])
            self.policy_loss.add_sample(losses[1])

            return total_loss, losses, unclipped_grads

        def get_prediction(self, states):
            tf_input_state = self.prepare_batch_for_inference(states, "main")
            return self.networks['main'].online_network.predict(tf_input_state)[1:]  # index 0 is the state value