code: def main():
      import logging
  
      parser = argparse.ArgumentParser()
      parser.add_argument("--env", type=str, default="Pendulum-v0")
      parser.add_argument(
          "--arch",
          type=str,
          default="Gaussian",
          choices=("FFSoftmax", "FFMellowmax", "Gaussian"),
      )
      parser.add_argument(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 32)"
      )
      parser.add_argument("--outdir", type=str, default=None)
      parser.add_argument("--profile", action="store_true")
      parser.add_argument("--steps", type=int, default=8 * 10**7)
      parser.add_argument("--update-steps", type=int, default=5)
      parser.add_argument("--log-interval", type=int, default=1000)
      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-5)
      parser.add_argument(
          "--render", action="store_true", default=False
      )
      parser.add_argument(
          "--gamma", type=float, default=0.99, help="discount factor"
      )
      parser.add_argument(
          "--use-gae",
          action="store_true",
          default=False,
          help="use generalized advantage estimation",
      )
      parser.add_argument(
          "--tau", type=float, default=0.95, help="gae parameter"
      )
      parser.add_argument("--lr", type=float, default=7e-4)
      parser.add_argument("--weight-decay", type=float, default=0.0)
      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(
          "--max-grad-norm",
          type=float,
          default=0.5,
          help="value loss coefficient",
      )
      parser.add_argument(
          "--alpha",
          type=float,
          default=0.99,
          help="RMSprop optimizer alpha",
      )
      parser.add_argument(
          "--gpu",
          "-g",
          type=int,
          default=-1,
          help="GPU ID (negative value indicates CPU)",
      )
      parser.add_argument("--num-envs", type=int, default=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.num_envs) + args.seed * args.num_envs
      )
      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)
          # Scale rewards observed by agents
          if not test:
              misc.env_modifiers.make_reward_filtered(
                  env, lambda x: x * args.reward_scale_factor
              )
          if args.render and process_idx == 0 and not test:
              env = chainerrl.wrappers.Render(env)
          return env
  
      def make_batch_env(test):
          return chainerrl.envs.MultiprocessVectorEnv(
              [
                  functools.partial(make_env, idx, test)
                  for idx, env in enumerate(range(args.num_envs))
              ]
          )
  
      sample_env = make_env(process_idx=0, test=False)
      timestep_limit = sample_env.spec.max_episode_steps
      obs_space = sample_env.observation_space
      action_space = sample_env.action_space
  
      # Switch policy types accordingly to action space types
      if args.arch == "Gaussian":
          model = A2CGaussian(obs_space.low.size, action_space.low.size)
      elif args.arch == "FFSoftmax":
          model = A2CFFSoftmax(obs_space.low.size, action_space.n)
      elif args.arch == "FFMellowmax":
          model = A2CFFMellowmax(obs_space.low.size, action_space.n)
  
      optimizer = chainer.optimizers.RMSprop(
          args.lr, eps=args.rmsprop_epsilon, alpha=args.alpha
      )
      optimizer.setup(model)
      optimizer.add_hook(
          chainer.optimizer.GradientClipping(args.max_grad_norm)
      )
      if args.weight_decay > 0:
          optimizer.add_hook(NonbiasWeightDecay(args.weight_decay))
  
      agent = a2c.A2C(
          model,
          optimizer,
          gamma=args.gamma,
          gpu=args.gpu,
          num_processes=args.num_envs,
          update_steps=args.update_steps,
          use_gae=args.use_gae,
          tau=args.tau,
      )
      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_batch_with_evaluation(
              agent=agent,
              env=make_batch_env(test=False),
              eval_env=make_batch_env(test=True),
              steps=args.steps,
              log_interval=args.log_interval,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              outdir=args.outdir,
          )
  
imports: import argparse
  import functools
  import chainer
  from chainer import functions
  import gym
  import numpy
  import chainerrl
  from chainerrl.agents import a2c
  from chainerrl import experiments
  from chainerrl import links
  from chainerrl import misc
  from chainerrl.optimizers.nonbias_weight_decay import NonbiasWeightDecay
  from chainerrl import policies
  from chainerrl import v_function
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env, chainerrl