text: # python3
  # 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.
  
  """Learner for the IMPALA actor-critic agent."""
  
  from typing import Callable, Dict, Iterator, List, NamedTuple, Tuple
  
  import acme
  from acme import specs
  from acme.jax import networks
  from acme.jax import utils
  from acme.utils import counting
  from acme.utils import loggers
  import haiku as hk
  import jax
  from jax.experimental import optix
  import jax.numpy as jnp
  import numpy as np
  import reverb
  import rlax
  import tree
  
  
  class TrainingState(NamedTuple):
    """Training state consists of network parameters and optimiser state."""
    params: hk.Params
    opt_state: optix.OptState
  
  
  class IMPALALearner(acme.Learner, acme.Saveable):
    """Learner for an importanced-weighted advantage actor-critic."""
  
    def __init__(
        self,
        obs_spec: specs.Array,
        unroll_fn: networks.PolicyValueRNN,
        initial_state_fn: Callable[[], hk.LSTMState],
        iterator: Iterator[reverb.ReplaySample],
        optimizer: optix.InitUpdate,
        rng: hk.PRNGSequence,
        discount: float = 0.99,
        entropy_cost: float = 0.,
        baseline_cost: float = 1.,
        max_abs_reward: float = np.inf,
        counter: counting.Counter = None,
        logger: loggers.Logger = None,
    ):
  
      # Transform into pure functions.
      unroll_fn = hk.without_apply_rng(hk.transform(unroll_fn, apply_rng=True))
      initial_state_fn = hk.without_apply_rng(
          hk.transform(initial_state_fn, apply_rng=True))
  
      def loss(params: hk.Params, sample: reverb.ReplaySample) -> jnp.ndarray:
        """Entropy-regularised actor-critic loss."""
  
        # Extract the data.
        data = sample.data
        observations, actions, rewards, discounts, extra = (data.observation,
                                                            data.action,
                                                            data.reward,
                                                            data.discount,
                                                            data.extras)
        initial_state = tree.map_structure(lambda s: s[0], extra['core_state'])
        behaviour_logits = extra['logits']
  
        # Apply reward clipping.
        rewards = jnp.clip(rewards, -max_abs_reward, max_abs_reward)
  
        # Unroll current policy over observations.
        (logits, values), _ = unroll_fn.apply(params, observations, initial_state)
  
        # Compute importance sampling weights: current policy / behavior policy.
        rhos = rlax.categorical_importance_sampling_ratios(
            logits[:-1], behaviour_logits[:-1], actions[:-1])
  
        # Critic loss.
        vtrace_returns = rlax.vtrace_td_error_and_advantage(
            v_tm1=values[:-1],
            v_t=values[1:],
            r_t=rewards[:-1],
            discount_t=discounts[:-1] * discount,
            rho_t=rhos)
        critic_loss = jnp.square(vtrace_returns.errors)
  
        # Policy gradient loss.
        policy_gradient_loss = rlax.policy_gradient_loss(
            logits_t=logits[:-1],
            a_t=actions[:-1],
            adv_t=vtrace_returns.pg_advantage,
            w_t=jnp.ones_like(rewards[:-1]))
  
        # Entropy regulariser.
        entropy_loss = rlax.entropy_loss(logits[:-1], jnp.ones_like(rewards[:-1]))
  
        # Combine weighted sum of actor & critic losses.
        mean_loss = jnp.mean(policy_gradient_loss + baseline_cost * critic_loss +
                             entropy_cost * entropy_loss)
  
        return mean_loss
  
      @jax.jit
      def sgd_step(
          state: TrainingState, sample: reverb.ReplaySample
      ) -> Tuple[TrainingState, Dict[str, jnp.ndarray]]:
        """Computes an SGD step, returning new state and metrics for logging."""
  
        # Compute gradients.
        batch_loss = jax.vmap(loss, in_axes=(None, 0))
        mean_loss = lambda p, s: jnp.mean(batch_loss(p, s))
        grad_fn = jax.value_and_grad(mean_loss)
        loss_value, gradients = grad_fn(state.params, sample)
  
        # Apply updates
        updates, new_opt_state = optimizer.update(gradients, state.opt_state)
        new_params = optix.apply_updates(state.params, updates)
  
        metrics = {
            'loss': loss_value,
        }
  
        new_state = TrainingState(params=new_params, opt_state=new_opt_state)
  
        return new_state, metrics
  
      def make_initial_state(key: jnp.ndarray) -> TrainingState:
        """Initialises the training state (parameters and optimiser state)."""
        dummy_obs = utils.zeros_like(obs_spec)
        dummy_obs = utils.add_batch_dim(dummy_obs)  # Dummy 'sequence' dim.
        initial_state = initial_state_fn.apply(None)
        initial_params = unroll_fn.init(key, dummy_obs, initial_state)
        initial_opt_state = optimizer.init(initial_params)
        return TrainingState(
            params=initial_params, opt_state=initial_opt_state)
  
      # Initialise training state (parameters and optimiser state).
      self._state = make_initial_state(next(rng))
  
      # Internalise iterator.
      self._iterator = iterator
      self._sgd_step = sgd_step
  
      # Set up logging/counting.
      self._counter = counter or counting.Counter()
      self._logger = logger or loggers.TerminalLogger('learner', time_delta=1.)
  
    def step(self):
      """Does a step of SGD and logs the results."""
  
      # Do a batch of SGD.
      sample = next(self._iterator)
      self._state, results = self._sgd_step(self._state, sample)
  
      # Update our counts and record it.
      counts = self._counter.increment(steps=1)
  
      # Snapshot and attempt to write logs.
      self._logger.write({**results, **counts})
  
    def get_variables(self, names: List[str]) -> List[hk.Params]:
      return [self._state.params]
  
    def save(self) -> TrainingState:
      return self._state
  
    def restore(self, state: TrainingState):
      self._state = state
  