code: def main():
      import logging
  
      parser = argparse.ArgumentParser()
      parser.add_argument("processes", type=int)
      parser.add_argument("--env", type=str, default="CartPole-v0")
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 32)"
      )
      parser.add_argument(
          "--outdir",
          type=str,
          default="results",
          help="Directory path to save output files."
          " If it does not exist, it will be created.",
      )
      parser.add_argument("--t-max", type=int, default=50)
      parser.add_argument("--n-times-replay", type=int, default=4)
      parser.add_argument("--n-hidden-channels", type=int, default=100)
      parser.add_argument("--n-hidden-layers", type=int, default=2)
      parser.add_argument("--replay-capacity", type=int, default=5000)
      parser.add_argument(
          "--replay-start-size", type=int, default=10**3
      )
      parser.add_argument(
          "--disable-online-update", action="store_true"
      )
      parser.add_argument("--beta", type=float, default=1e-2)
      parser.add_argument("--profile", action="store_true")
      parser.add_argument("--steps", type=int, default=8 * 10**7)
      parser.add_argument("--eval-interval", type=int, default=10**5)
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument(
          "--reward-scale-factor", type=float, default=1e-2
      )
      parser.add_argument("--rmsprop-epsilon", type=float, default=1e-2)
      parser.add_argument(
          "--render", action="store_true", default=False
      )
      parser.add_argument("--lr", type=float, default=7e-4)
      parser.add_argument("--demo", action="store_true", default=False)
      parser.add_argument("--load", type=str, default="")
      parser.add_argument(
          "--logger-level", type=int, default=logging.DEBUG
      )
      parser.add_argument("--monitor", action="store_true")
      parser.add_argument(
          "--truncation-threshold", type=float, default=5
      )
      parser.add_argument(
          "--trust-region-delta", type=float, default=0.1
      )
      args = parser.parse_args()
  
      logging.basicConfig(level=args.logger_level)
  
      # Set a random seed used in ChainerRL.
      # If you use more than one processes, the results will be no longer
      # deterministic even with the same random seed.
      misc.set_random_seed(args.seed)
  
      # Set different random seeds for different subprocesses.
      # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
      # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
      process_seeds = (
          np.arange(args.processes) + args.seed * args.processes
      )
      assert process_seeds.max() < 2**32
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
  
      def make_env(process_idx, test):
          env = gym.make(args.env)
          # Use different random seeds for train and test envs
          process_seed = int(process_seeds[process_idx])
          env_seed = (
              2**32 - 1 - process_seed if test else process_seed
          )
          env.seed(env_seed)
          # Cast observations to float32 because our model uses float32
          env = chainerrl.wrappers.CastObservationToFloat32(env)
          if args.monitor and process_idx == 0:
              env = chainerrl.wrappers.Monitor(env, args.outdir)
          if not test:
              # Scale rewards (and thus returns) to a reasonable range so that
              # training is easier
              env = chainerrl.wrappers.ScaleReward(
                  env, args.reward_scale_factor
              )
          if args.render and process_idx == 0 and not test:
              env = chainerrl.wrappers.Render(env)
          return env
  
      sample_env = gym.make(args.env)
      timestep_limit = sample_env.spec.max_episode_steps
      obs_space = sample_env.observation_space
      action_space = sample_env.action_space
  
      if isinstance(action_space, spaces.Box):
          model = acer.ACERSDNSeparateModel(
              pi=policies.FCGaussianPolicy(
                  obs_space.low.size,
                  action_space.low.size,
                  n_hidden_channels=args.n_hidden_channels,
                  n_hidden_layers=args.n_hidden_layers,
                  bound_mean=True,
                  min_action=action_space.low,
                  max_action=action_space.high,
              ),
              v=v_functions.FCVFunction(
                  obs_space.low.size,
                  n_hidden_channels=args.n_hidden_channels,
                  n_hidden_layers=args.n_hidden_layers,
              ),
              adv=q_functions.FCSAQFunction(
                  obs_space.low.size,
                  action_space.low.size,
                  n_hidden_channels=args.n_hidden_channels // 4,
                  n_hidden_layers=args.n_hidden_layers,
              ),
          )
      else:
          model = acer.ACERSeparateModel(
              pi=links.Sequence(
                  L.Linear(obs_space.low.size, args.n_hidden_channels),
                  F.relu,
                  L.Linear(
                      args.n_hidden_channels,
                      action_space.n,
                      initialW=LeCunNormal(1e-3),
                  ),
                  SoftmaxDistribution,
              ),
              q=links.Sequence(
                  L.Linear(obs_space.low.size, args.n_hidden_channels),
                  F.relu,
                  L.Linear(
                      args.n_hidden_channels,
                      action_space.n,
                      initialW=LeCunNormal(1e-3),
                  ),
                  DiscreteActionValue,
              ),
          )
  
      opt = rmsprop_async.RMSpropAsync(
          lr=args.lr, eps=args.rmsprop_epsilon, alpha=0.99
      )
      opt.setup(model)
      opt.add_hook(chainer.optimizer.GradientClipping(40))
  
      replay_buffer = EpisodicReplayBuffer(args.replay_capacity)
      agent = acer.ACER(
          model,
          opt,
          t_max=args.t_max,
          gamma=0.99,
          replay_buffer=replay_buffer,
          n_times_replay=args.n_times_replay,
          replay_start_size=args.replay_start_size,
          disable_online_update=args.disable_online_update,
          use_trust_region=True,
          trust_region_delta=args.trust_region_delta,
          truncation_threshold=args.truncation_threshold,
          beta=args.beta,
      )
      if args.load:
          agent.load(args.load)
  
      if args.demo:
          env = make_env(0, True)
          eval_stats = experiments.eval_performance(
              env=env,
              agent=agent,
              n_steps=None,
              n_episodes=args.eval_n_runs,
              max_episode_len=timestep_limit,
          )
          print(
              "n_runs: {} mean: {} median: {} stdev {}".format(
                  args.eval_n_runs,
                  eval_stats["mean"],
                  eval_stats["median"],
                  eval_stats["stdev"],
              )
          )
      else:
          experiments.train_agent_async(
              agent=agent,
              outdir=args.outdir,
              processes=args.processes,
              make_env=make_env,
              profile=args.profile,
              steps=args.steps,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              max_episode_len=timestep_limit,
          )
  
imports: import argparse
  import os
  import chainer
  from chainer import functions
  from chainer.initializers import LeCunNormal
  from chainer import links
  import gym
  from gym import spaces
  import numpy
  import chainerrl
  from chainerrl.action_value import DiscreteActionValue
  from chainerrl.agents import acer
  from chainerrl.distribution import SoftmaxDistribution
  from chainerrl import experiments
  from chainerrl import links
  from chainerrl import misc
  from chainerrl.optimizers import rmsprop_async
  from chainerrl import policies
  from chainerrl import q_functions
  from chainerrl.replay_buffer import EpisodicReplayBuffer
  from chainerrl import v_functions
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env