# Copyright 2018 DeepMind Technologies Limited. All rights reserved.
#
# 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.

"""MPO agent implementation."""

import copy
from typing import Optional

from acme import datasets
from acme import specs
from acme import types
from acme.adders import reverb as adders
from acme.agents import agent
from acme.agents.tf import actors
from Agents.MPO.learning import MPOLearner
from acme.tf import networks
from acme.tf import utils as tf2_utils
from acme.utils import counting
from acme.utils import loggers
import reverb
import sonnet as snt
import tensorflow as tf


class MPO(agent.Agent):
    """MPO Agent.

    This implements a single-process MPO agent. This is an actor-critic algorithm
    that generates data via a behavior policy, inserts N-step transitions into
    a replay buffer, and periodically updates the policy (and as a result the
    behavior) by sampling uniformly from this buffer. This agent distinguishes
    itself from the DPG agent by using MPO to learn a stochastic policy.
    """

    def __init__(
        self,
        environment_spec: specs.EnvironmentSpec,
        policy_network: snt.Module,
        critic_network: snt.Module,
        observation_network: types.TensorTransformation = tf.identity,
        discount: float = 0.99,
        batch_size: int = 256,
        prefetch_size: int = 4,
        target_policy_update_period: int = 100,
        target_critic_update_period: int = 100,
        min_replay_size: int = 1000,
        max_replay_size: int = 1000000,
        samples_per_insert: float = 32.0,
        policy_loss_module: Optional[snt.Module] = None,
        policy_optimizer: Optional[snt.Optimizer] = None,
        critic_optimizer: Optional[snt.Optimizer] = None,
        n_step: int = 5,
        num_samples: int = 20,
        clipping: bool = True,
        logger: Optional[loggers.Logger] = None,
        counter: Optional[counting.Counter] = None,
        checkpoint: bool = True,
        save_directory: str = '~/acme',
        replay_table_name: str = adders.DEFAULT_PRIORITY_TABLE,
    ):
        """Initialize the agent.

        Args:
            environment_spec: description of the actions, observations, etc.
            policy_network: the online (optimized) policy.
            critic_network: the online critic.
            observation_network: optional network to transform the observations before
            they are fed into any network.
            discount: discount to use for TD updates.
            batch_size: batch size for updates.
            prefetch_size: size to prefetch from replay.
            target_policy_update_period: number of updates to perform before updating
            the target policy network.
            target_critic_update_period: number of updates to perform before updating
            the target critic network.
            min_replay_size: minimum replay size before updating.
            max_replay_size: maximum replay size.
            samples_per_insert: number of samples to take from replay for every insert
            that is made.
            policy_loss_module: configured MPO loss function for the policy
            optimization; defaults to sensible values on the control suite. See
            `acme/tf/losses/mpo.py` for more details.
            policy_optimizer: optimizer to be used on the policy.
            critic_optimizer: optimizer to be used on the critic.
            n_step: number of steps to squash into a single transition.
            num_samples: number of actions to sample when doing a Monte Carlo
            integration with respect to the policy.
            clipping: whether to clip gradients by global norm.
            logger: logging object used to write to logs.
            counter: counter object used to keep track of steps.
            checkpoint: boolean indicating whether to checkpoint the learner.
            save_directory: string indicating where the learner should save
            checkpoints and snapshots.
            replay_table_name: string indicating what name to give the replay table.
        """

        # Create a replay server to add data to.
        replay_table = reverb.Table(
            name=adders.DEFAULT_PRIORITY_TABLE,
            sampler=reverb.selectors.Uniform(),
            remover=reverb.selectors.Fifo(),
            max_size=max_replay_size,
            rate_limiter=reverb.rate_limiters.MinSize(min_size_to_sample=1),
            signature=adders.NStepTransitionAdder.signature(environment_spec))
        self._server = reverb.Server([replay_table], port=None)

        # The adder is used to insert observations into replay.
        address = f'localhost:{self._server.port}'
        adder = adders.NStepTransitionAdder(
            client=reverb.Client(address), n_step=n_step, discount=discount)

        # The dataset object to learn from.
        dataset = datasets.make_reverb_dataset(
            table=replay_table_name,
            server_address=address,
            batch_size=batch_size,
            prefetch_size=prefetch_size)

        # Make sure observation network is a Sonnet Module.
        observation_network = tf2_utils.to_sonnet_module(observation_network)

        # Create target networks before creating online/target network variables.
        target_policy_network = copy.deepcopy(policy_network)
        target_critic_network = copy.deepcopy(critic_network)
        target_observation_network = copy.deepcopy(observation_network)

        # Get observation and action specs.
        act_spec = environment_spec.actions
        obs_spec = environment_spec.observations
        emb_spec = tf2_utils.create_variables(observation_network, [obs_spec])

        # Create the behavior policy.
        behavior_network = snt.Sequential([
            observation_network,
            policy_network,
            networks.StochasticSamplingHead(),
        ])

        # Create variables.
        tf2_utils.create_variables(policy_network, [emb_spec])
        tf2_utils.create_variables(critic_network, [emb_spec, act_spec])
        tf2_utils.create_variables(target_policy_network, [emb_spec])
        tf2_utils.create_variables(target_critic_network, [emb_spec, act_spec])
        tf2_utils.create_variables(target_observation_network, [obs_spec])

        # Create the actor which defines how we take actions.
        actor = actors.FeedForwardActor(
            policy_network=behavior_network, adder=adder)

        # Create optimizers.
        policy_optimizer = policy_optimizer or snt.optimizers.Adam(1e-4)
        critic_optimizer = critic_optimizer or snt.optimizers.Adam(1e-4)

        # The learner updates the parameters (and initializes them).
        learner = MPOLearner(
            policy_network=policy_network,
            critic_network=critic_network,
            observation_network=observation_network,
            target_policy_network=target_policy_network,
            target_critic_network=target_critic_network,
            target_observation_network=target_observation_network,
            policy_loss_module=policy_loss_module,
            policy_optimizer=policy_optimizer,
            critic_optimizer=critic_optimizer,
            clipping=clipping,
            discount=discount,
            num_samples=num_samples,
            target_policy_update_period=target_policy_update_period,
            target_critic_update_period=target_critic_update_period,
            dataset=dataset,
            logger=logger,
            counter=counter,
            checkpoint=checkpoint,
            save_directory=save_directory)

        super().__init__(
            actor=actor,
            learner=learner,
            min_observations=max(batch_size, min_replay_size),
            observations_per_step=float(batch_size) / samples_per_insert)