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.
  
  """Shared helpers for different experiment flavours."""
  
  from typing import Mapping, Sequence
  
  from acme import specs
  from acme import types
  from acme import wrappers
  from acme.tf import networks
  from acme.tf import utils as tf2_utils
  from dm_control import suite
  import dm_env
  import numpy as np
  import sonnet as snt
  
  
  def make_environment(evaluation: bool = False,
                       domain_name: str = 'cartpole',
                       task_name: str = 'balance') -> dm_env.Environment:
    """Implements a control suite environment factory."""
    # Nothing special to be done for evaluation environment.
    del evaluation
  
    environment = suite.load(domain_name, task_name)
    environment = wrappers.SinglePrecisionWrapper(environment)
  
    return environment
  
  
  def make_networks(
      action_spec: specs.BoundedArray,
      policy_layer_sizes: Sequence[int] = (256, 256, 256),
      critic_layer_sizes: Sequence[int] = (512, 512, 256),
      vmin: float = -150.,
      vmax: float = 150.,
      num_atoms: int = 51,
  ) -> Mapping[str, types.TensorTransformation]:
    """Creates networks used by the agent."""
  
    # Get total number of action dimensions from action spec.
    num_dimensions = np.prod(action_spec.shape, dtype=int)
  
    # Create the shared observation network; here simply a state-less operation.
    observation_network = tf2_utils.batch_concat
  
    # Create the policy network.
    policy_network = snt.Sequential([
        networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
        networks.NearZeroInitializedLinear(num_dimensions),
        networks.TanhToSpec(action_spec),
    ])
  
    # Create the critic network.
    critic_network = snt.Sequential([
        # The multiplexer concatenates the observations/actions.
        networks.CriticMultiplexer(),
        networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
        networks.DiscreteValuedHead(vmin, vmax, num_atoms),
    ])
  
    return {
        'policy': policy_network,
        'critic': critic_network,
        'observation': observation_network,
    }
  