code: def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--env", type=str, default="BreakoutNoFrameskip-v4"
      )
      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(
          "--seed", type=int, default=0, help="Random seed [0, 2 ** 31)"
      )
      parser.add_argument("--gpu", type=int, default=0)
      parser.add_argument("--demo", action="store_true", default=False)
      parser.add_argument("--load", type=str, default=None)
      parser.add_argument(
          "--use-sdl", action="store_true", default=False
      )
      parser.add_argument(
          "--final-exploration-frames", type=int, default=10**6
      )
      parser.add_argument("--final-epsilon", type=float, default=0.1)
      parser.add_argument("--eval-epsilon", type=float, default=0.05)
      parser.add_argument("--steps", type=int, default=10**7)
      parser.add_argument(
          "--max-frames",
          type=int,
          default=30 * 60 * 60,  # 30 minutes with 60 fps
          help="Maximum number of frames for each episode.",
      )
      parser.add_argument(
          "--replay-start-size", type=int, default=5 * 10**4
      )
      parser.add_argument(
          "--target-update-interval", type=int, default=10**4
      )
      parser.add_argument("--eval-interval", type=int, default=10**5)
      parser.add_argument("--update-interval", type=int, default=4)
      parser.add_argument("--eval-n-runs", type=int, default=10)
      parser.add_argument("--batch-size", type=int, default=32)
      parser.add_argument(
          "--logging-level",
          type=int,
          default=20,
          help="Logging level. 10:DEBUG, 20:INFO etc.",
      )
      parser.add_argument(
          "--render",
          action="store_true",
          default=False,
          help="Render env states in a GUI window.",
      )
      parser.add_argument(
          "--monitor",
          action="store_true",
          default=False,
          help="Monitor env. Videos and additional information"
          " are saved as output files.",
      )
      args = parser.parse_args()
  
      import logging
  
      logging.basicConfig(level=args.logging_level)
  
      # Set a random seed used in ChainerRL.
      misc.set_random_seed(args.seed, gpus=(args.gpu,))
  
      # Set different random seeds for train and test envs.
      train_seed = args.seed
      test_seed = 2**31 - 1 - args.seed
  
      args.outdir = experiments.prepare_output_dir(args, args.outdir)
      print("Output files are saved in {}".format(args.outdir))
  
      def make_env(test):
          # Use different random seeds for train and test envs
          env_seed = test_seed if test else train_seed
          env = atari_wrappers.wrap_deepmind(
              atari_wrappers.make_atari(
                  args.env, max_frames=args.max_frames
              ),
              episode_life=not test,
              clip_rewards=not test,
          )
          env.seed(int(env_seed))
          if test:
              # Randomize actions like epsilon-greedy in evaluation as well
              env = chainerrl.wrappers.RandomizeAction(
                  env, args.eval_epsilon
              )
          if args.monitor:
              env = chainerrl.wrappers.Monitor(
                  env,
                  args.outdir,
                  mode="evaluation" if test else "training",
              )
          if args.render:
              env = chainerrl.wrappers.Render(env)
          return env
  
      env = make_env(test=False)
      eval_env = make_env(test=True)
  
      n_actions = env.action_space.n
  
      n_atoms = 51
      v_max = 10
      v_min = -10
      q_func = chainerrl.links.Sequence(
          chainerrl.links.NatureDQNHead(),
          chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
              None,
              n_actions,
              n_atoms,
              v_min,
              v_max,
              n_hidden_channels=0,
              n_hidden_layers=0,
          ),
      )
  
      # Draw the computational graph and save it in the output directory.
      chainerrl.misc.draw_computational_graph(
          [q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
          os.path.join(args.outdir, "model"),
      )
  
      # Use the same hyper parameters as https://arxiv.org/abs/1707.06887
      opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size)
      opt.setup(q_func)
  
      rbuf = replay_buffer.ReplayBuffer(10**6)
  
      explorer = explorers.LinearDecayEpsilonGreedy(
          1.0,
          args.final_epsilon,
          args.final_exploration_frames,
          lambda: np.random.randint(n_actions),
      )
  
      def phi(x):
          # Feature extractor
          return np.asarray(x, dtype=np.float32) / 255
  
      agent = chainerrl.agents.CategoricalDQN(
          q_func,
          opt,
          rbuf,
          gpu=args.gpu,
          gamma=0.99,
          explorer=explorer,
          replay_start_size=args.replay_start_size,
          target_update_interval=args.target_update_interval,
          update_interval=args.update_interval,
          batch_accumulator="mean",
          phi=phi,
      )
  
      if args.load:
          agent.load(args.load)
  
      if args.demo:
          eval_stats = experiments.eval_performance(
              env=eval_env,
              agent=agent,
              n_steps=None,
              n_episodes=args.eval_n_runs,
          )
          print(
              "n_runs: {} mean: {} median: {} stdev {}".format(
                  args.eval_n_runs,
                  eval_stats["mean"],
                  eval_stats["median"],
                  eval_stats["stdev"],
              )
          )
      else:
          experiments.train_agent_with_evaluation(
              agent=agent,
              env=env,
              steps=args.steps,
              eval_n_steps=None,
              eval_n_episodes=args.eval_n_runs,
              eval_interval=args.eval_interval,
              outdir=args.outdir,
              save_best_so_far_agent=False,
              eval_env=eval_env,
          )
  
file_path: chainerrl/examples/atari/train_categorical_dqn_ale.py
imports: import argparse
  import os
  import chainer
  import numpy
  import chainerrl
  from chainerrl import experiments
  from chainerrl import explorers
  from chainerrl import misc
  from chainerrl import replay_buffer
  from chainerrl.wrappers import atari_wrappers
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env, unknown