# 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.

"""DQfD Agent implementation."""

import copy
import functools
import operator
from typing import Optional

from acme import datasets
from acme import specs
from acme import types as acme_types
from acme.adders import reverb as adders
from acme.agents import agent
from acme.agents.tf import actors
from acme.agents.tf import dqn
from acme.tf import utils as tf2_utils
import reverb
import sonnet as snt
import tensorflow as tf
import tree
import trfl


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

  This implements a single-process DQN agent that mixes demonstrations with
  actor experience.
  """

  def __init__(
      self,
      environment_spec: specs.EnvironmentSpec,
      network: snt.Module,
      demonstration_dataset: tf.data.Dataset,
      demonstration_ratio: float,
      batch_size: int = 256,
      prefetch_size: int = 4,
      target_update_period: int = 100,
      samples_per_insert: float = 32.0,
      min_replay_size: int = 1000,
      max_replay_size: int = 1000000,
      importance_sampling_exponent: float = 0.2,
      n_step: int = 5,
      epsilon: Optional[tf.Tensor] = None,
      learning_rate: float = 1e-3,
      discount: float = 0.99,
  ):
    """Initialize the agent.

    Args:
      environment_spec: description of the actions, observations, etc.
      network: the online Q network (the one being optimized)
      demonstration_dataset: tf.data.Dataset producing (timestep, action)
        tuples containing full episodes.
      demonstration_ratio: Ratio of transitions coming from demonstrations.
      batch_size: batch size for updates.
      prefetch_size: size to prefetch from replay.
      target_update_period: number of learner steps to perform before updating
        the target networks.
      samples_per_insert: number of samples to take from replay for every insert
        that is made.
      min_replay_size: minimum replay size before updating. This and all
        following arguments are related to dataset construction and will be
        ignored if a dataset argument is passed.
      max_replay_size: maximum replay size.
      importance_sampling_exponent: power to which importance weights are raised
        before normalizing.
      n_step: number of steps to squash into a single transition.
      epsilon: probability of taking a random action; ignored if a policy
        network is given.
      learning_rate: learning rate for the q-network update.
      discount: discount to use for TD updates.
    """

    # Create a replay server to add data to. This uses no limiter behavior in
    # order to allow the Agent interface to handle it.
    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(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 provides an interface to sample from replay.
    replay_client = reverb.TFClient(address)
    dataset = datasets.make_reverb_dataset(server_address=address)

    # Combine with demonstration dataset.
    transition = functools.partial(_n_step_transition_from_episode,
                                   n_step=n_step,
                                   discount=discount)
    dataset_demos = demonstration_dataset.map(transition)
    dataset = tf.data.experimental.sample_from_datasets(
        [dataset, dataset_demos],
        [1 - demonstration_ratio, demonstration_ratio])

    # Batch and prefetch.
    dataset = dataset.batch(batch_size, drop_remainder=True)
    dataset = dataset.prefetch(prefetch_size)

    # Use constant 0.05 epsilon greedy policy by default.
    if epsilon is None:
      epsilon = tf.Variable(0.05, trainable=False)
    policy_network = snt.Sequential([
        network,
        lambda q: trfl.epsilon_greedy(q, epsilon=epsilon).sample(),
    ])

    # Create a target network.
    target_network = copy.deepcopy(network)

    # Ensure that we create the variables before proceeding (maybe not needed).
    tf2_utils.create_variables(network, [environment_spec.observations])
    tf2_utils.create_variables(target_network, [environment_spec.observations])

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

    # The learner updates the parameters (and initializes them).
    learner = dqn.DQNLearner(
        network=network,
        target_network=target_network,
        discount=discount,
        importance_sampling_exponent=importance_sampling_exponent,
        learning_rate=learning_rate,
        target_update_period=target_update_period,
        dataset=dataset,
        replay_client=replay_client)

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


def _n_step_transition_from_episode(observations: acme_types.NestedTensor,
                                    actions: tf.Tensor,
                                    rewards: tf.Tensor,
                                    discounts: tf.Tensor,
                                    n_step: int,
                                    discount: float):
  """Produce Reverb-like N-step transition from a full episode.

  Observations, actions, rewards and discounts have the same length. This
  function will ignore the first reward and discount and the last action.

  Args:
    observations: [L, ...] Tensor.
    actions: [L, ...] Tensor.
    rewards: [L] Tensor.
    discounts: [L] Tensor.
    n_step: number of steps to squash into a single transition.
    discount: discount to use for TD updates.

  Returns:
    (o_t, a_t, r_t, d_t, o_tp1) tuple.
  """

  max_index = tf.shape(rewards)[0] - 1
  first = tf.random.uniform(shape=(), minval=0, maxval=max_index - 1,
                            dtype=tf.int32)
  last = tf.minimum(first + n_step, max_index)

  o_t = tree.map_structure(operator.itemgetter(first), observations)
  a_t = tree.map_structure(operator.itemgetter(first), actions)
  o_tp1 = tree.map_structure(operator.itemgetter(last), observations)

  # 0, 1, ..., n-1.
  discount_range = tf.cast(tf.range(last - first), tf.float32)
  # 1, g, ..., g^{n-1}.
  additional_discounts = tf.pow(discount, discount_range)
  # 1, d_t, d_t * d_{t+1}, ..., d_t * ... * d_{t+n-2}.
  discounts = tf.concat([[1.], tf.math.cumprod(discounts[first:last-1])], 0)
  # 1, g * d_t, ..., g^{n-1} * d_t * ... * d_{t+n-2}.
  discounts *= additional_discounts
  # r_t + g * d_t * r_{t+1} + ... + g^{n-1} * d_t * ... * d_{t+n-2} * r_{t+n-1}
  # We have to shift rewards by one so last=max_index corresponds to transitions
  # that include the last reward.
  r_t = tf.reduce_sum(rewards[first+1:last+1] * discounts)

  # g^{n-1} * d_{t} * ... * d_{t+n-1}.
  d_t = discounts[-1]

  info = tree.map_structure(lambda dtype: tf.ones([], dtype),
                            reverb.SampleInfo.tf_dtypes())
  return reverb.ReplaySample(
      info=info, data=acme_types.Transition(o_t, a_t, r_t, d_t, o_tp1))
