text: # python3
  # Copyright 2021 InstaDeep Ltd. 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.
  
  """Example running MAD4PG on debug MPE environments."""
  import functools
  from datetime import datetime
  from typing import Any
  
  import launchpad as lp
  import sonnet as snt
  from absl import app, flags
  
  from mava.components.tf import architectures
  from mava.systems.tf import mad4pg
  from mava.utils import lp_utils
  from mava.utils.environments import debugging_utils
  from mava.utils.loggers import logger_utils
  
  FLAGS = flags.FLAGS
  flags.DEFINE_string(
      "env_name",
      "simple_spread",
      "Debugging environment name (str).",
  )
  flags.DEFINE_string(
      "action_space",
      "continuous",
      "Environment action space type (str).",
  )
  flags.DEFINE_string(
      "mava_id",
      str(datetime.now()),
      "Experiment identifier that can be used to continue experiments.",
  )
  flags.DEFINE_string("base_dir", "~/mava", "Base dir to store experiments.")
  
  
  def main(_: Any) -> None:
  
      # Environment.
      environment_factory = functools.partial(
          debugging_utils.make_environment,
          env_name=FLAGS.env_name,
          action_space=FLAGS.action_space,
          return_state_info=True,
      )
  
      # Networks.
      network_factory = lp_utils.partial_kwargs(
          mad4pg.make_default_networks,
      )
  
      # Checkpointer appends "Checkpoints" to checkpoint_dir.
      checkpoint_dir = f"{FLAGS.base_dir}/{FLAGS.mava_id}"
  
      # Log every [log_every] seconds.
      log_every = 10
      logger_factory = functools.partial(
          logger_utils.make_logger,
          directory=FLAGS.base_dir,
          to_terminal=True,
          to_tensorboard=True,
          time_stamp=FLAGS.mava_id,
          time_delta=log_every,
      )
  
      # Distributed program.
      program = mad4pg.MAD4PG(
          environment_factory=environment_factory,
          network_factory=network_factory,
          logger_factory=logger_factory,
          num_executors=1,
          policy_optimizer=snt.optimizers.Adam(learning_rate=1e-4),
          critic_optimizer=snt.optimizers.Adam(learning_rate=1e-4),
          checkpoint_subpath=checkpoint_dir,
          max_gradient_norm=40.0,
          trainer_fn=mad4pg.MAD4PGStateBasedTrainer,
          architecture=architectures.StateBasedQValueCritic,
          shared_weights=False,
      ).build()
  
      # Ensure only trainer runs on gpu, while other processes run on cpu.
      local_resources = lp_utils.to_device(
          program_nodes=program.groups.keys(), nodes_on_gpu=["trainer"]
      )
  
      # Launch.
      lp.launch(
          program,
          lp.LaunchType.LOCAL_MULTI_PROCESSING,
          terminal="current_terminal",
          local_resources=local_resources,
      )
  
  
  if __name__ == "__main__":
      app.run(main)
  