text: # 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.
  
  """Integration test for the distributed agent."""
  
  from typing import Sequence
  
  import acme
  from acme import specs
  from acme.agents.tf import mpo
  from acme.testing import fakes
  from acme.tf import networks
  from acme.tf import utils as tf2_utils
  import launchpad as lp
  import numpy as np
  import sonnet as snt
  
  from absl.testing import absltest
  
  
  def make_networks(
      action_spec: specs.BoundedArray,
      policy_layer_sizes: Sequence[int] = (50, 50),
      critic_layer_sizes: Sequence[int] = (50, 50),
  ):
    """Creates networks used by the agent."""
  
    num_dimensions = np.prod(action_spec.shape, dtype=int)
  
    observation_network = tf2_utils.batch_concat
    policy_network = snt.Sequential([
        networks.LayerNormMLP(policy_layer_sizes, activate_final=True),
        networks.MultivariateNormalDiagHead(
            num_dimensions,
            tanh_mean=True,
            init_scale=0.3,
            fixed_scale=True,
            use_tfd_independent=False)
    ])
    evaluator_network = snt.Sequential([
        observation_network,
        policy_network,
        networks.StochasticMeanHead(),
    ])
    # The multiplexer concatenates the (maybe transformed) observations/actions.
    multiplexer = networks.CriticMultiplexer(
        action_network=networks.ClipToSpec(action_spec))
    critic_network = snt.Sequential([
        multiplexer,
        networks.LayerNormMLP(critic_layer_sizes, activate_final=True),
        networks.NearZeroInitializedLinear(1),
    ])
  
    return {
        'policy': policy_network,
        'critic': critic_network,
        'observation': observation_network,
        'evaluator': evaluator_network,
    }
  
  
  class DistributedAgentTest(absltest.TestCase):
    """Simple integration/smoke test for the distributed agent."""
  
    def test_agent(self):
  
      agent = mpo.DistributedMPO(
          environment_factory=lambda x: fakes.ContinuousEnvironment(bounded=True),
          network_factory=make_networks,
          num_actors=2,
          batch_size=32,
          min_replay_size=32,
          max_replay_size=1000,
      )
      program = agent.build()
  
      (learner_node,) = program.groups['learner']
      learner_node.disable_run()
  
      lp.launch(program, launch_type='test_mt')
  
      learner: acme.Learner = learner_node.create_handle().dereference()
  
      for _ in range(5):
        learner.step()
  
  
  if __name__ == '__main__':
    absltest.main()
  