code: # 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.
  
  """Tests for the D4PG agent."""
  
  import sys
  from typing import Dict, Sequence
  
  from absl.testing import absltest
  import acme
  from acme import specs
  from acme import types
  from acme.agents.tf import d4pg
  from acme.testing import fakes
  from acme.tf import networks
  import numpy as np
  import sonnet as snt
  import tensorflow as tf
  
  
  def make_networks(
      action_spec: types.NestedSpec,
      policy_layer_sizes: Sequence[int] = (10, 10),
      critic_layer_sizes: Sequence[int] = (10, 10),
      vmin: float = -150.,
      vmax: float = 150.,
      num_atoms: int = 51,
  ) -> Dict[str, snt.Module]:
    """Creates networks used by the agent."""
  
    num_dimensions = np.prod(action_spec.shape, dtype=int)
    policy_layer_sizes = list(policy_layer_sizes) + [num_dimensions]
  
    policy_network = snt.Sequential(
        [networks.LayerNormMLP(policy_layer_sizes), tf.tanh])
    critic_network = snt.Sequential([
        networks.CriticMultiplexer(
            critic_network=networks.LayerNormMLP(
                critic_layer_sizes, activate_final=True)),
        networks.DiscreteValuedHead(vmin, vmax, num_atoms)
    ])
  
    return {
        'policy': policy_network,
        'critic': critic_network,
    }
  
  
  class D4PGTest(absltest.TestCase):
  
    def test_d4pg(self):
      # Create a fake environment to test with.
      environment = fakes.ContinuousEnvironment(episode_length=10, bounded=True)
      spec = specs.make_environment_spec(environment)
  
      # Create the networks.
      agent_networks = make_networks(spec.actions)
  
      # Construct the agent.
      agent = d4pg.D4PG(
          environment_spec=spec,
          policy_network=agent_networks['policy'],
          critic_network=agent_networks['critic'],
          batch_size=10,
          samples_per_insert=2,
          min_replay_size=10,
      )
  
      # Try running the environment loop. We have no assertions here because all
      # we care about is that the agent runs without raising any errors.
      loop = acme.EnvironmentLoop(environment, agent)
      loop.run(num_episodes=2)
      # Imports check
  
  
  if __name__ == '__main__':
    absltest.main()
  
apis: numpy.prod