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-pretrained", action="store_true", default=False
      )
      parser.add_argument(
          "--pretrained-type",
          type=str,
          default="best",
          choices=["best", "final"],
      )
      parser.add_argument("--load", type=str, default=None)
      parser.add_argument("--eval-epsilon", type=float, default=0.0)
      parser.add_argument("--noisy-net-sigma", type=float, default=0.5)
      parser.add_argument("--steps", type=int, default=5 * 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=2 * 10**4
      )
      parser.add_argument("--eval-n-steps", type=int, default=125000)
      parser.add_argument("--eval-interval", type=int, default=250000)
      parser.add_argument(
          "--log-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."
          ),
      )
      parser.add_argument("--n-best-episodes", type=int, default=200)
      args = parser.parse_args()
  
      import logging
  
      logging.basicConfig(level=args.log_level)
  
      # Set a random seed used in PFRL.
      utils.set_random_seed(args.seed)
  
      # 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 = pfrl.wrappers.RandomizeAction(
                  env, args.eval_epsilon
              )
          if args.monitor:
              env = pfrl.wrappers.Monitor(
                  env,
                  args.outdir,
                  mode="evaluation" if test else "training",
              )
          if args.render:
              env = pfrl.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 = DistributionalDuelingDQN(
          n_actions,
          n_atoms,
          v_min,
          v_max,
      )
  
      # Noisy nets
      pnn.to_factorized_noisy(q_func, sigma_scale=args.noisy_net_sigma)
      # Turn off explorer
      explorer = explorers.Greedy()
  
      # Use the same hyper parameters as https://arxiv.org/abs/1710.02298
      opt = torch.optim.Adam(
          q_func.parameters(), 6.25e-5, eps=1.5 * 10**-4
      )
  
      # Prioritized Replay
      # Anneal beta from beta0 to 1 throughout training
      update_interval = 4
      betasteps = args.steps / update_interval
      rbuf = replay_buffers.PrioritizedReplayBuffer(
          10**6,
          alpha=0.5,
          beta0=0.4,
          betasteps=betasteps,
          num_steps=3,
          normalize_by_max="memory",
      )
  
      def phi(x):
          # Feature extractor
          return np.asarray(x, dtype=np.float32) / 255
  
      Agent = agents.CategoricalDoubleDQN
      agent = Agent(
          q_func,
          opt,
          rbuf,
          gpu=args.gpu,
          gamma=0.99,
          explorer=explorer,
          minibatch_size=32,
          replay_start_size=args.replay_start_size,
          target_update_interval=32000,
          update_interval=update_interval,
          batch_accumulator="mean",
          phi=phi,
      )
  
      if args.load or args.load_pretrained:
          # either load_ or load_pretrained must be false
          assert not args.load or not args.load_pretrained
          if args.load:
              agent.load(args.load)
          else:
              agent.load(
                  utils.download_model(
                      "Rainbow",
                      args.env,
                      model_type=args.pretrained_type,
                  )[0]
              )
  
      if args.demo:
          eval_stats = experiments.eval_performance(
              env=eval_env,
              agent=agent,
              n_steps=args.eval_n_steps,
              n_episodes=None,
          )
          print(
              "n_episodes: {} mean: {} median: {} stdev {}".format(
                  eval_stats["episodes"],
                  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=args.eval_n_steps,
              eval_n_episodes=None,
              eval_interval=args.eval_interval,
              outdir=args.outdir,
              save_best_so_far_agent=True,
              eval_env=eval_env,
          )
  
          dir_of_best_network = os.path.join(args.outdir, "best")
          agent.load(dir_of_best_network)
  
          # run 200 evaluation episodes, each capped at 30 mins of play
          stats = experiments.evaluator.eval_performance(
              env=eval_env,
              agent=agent,
              n_steps=None,
              n_episodes=args.n_best_episodes,
              max_episode_len=args.max_frames / 4,
              logger=None,
          )
          with open(
              os.path.join(args.outdir, "bestscores.json"), "w"
          ) as f:
              json.dump(stats, f)
          print("The results of the best scoring network:")
          for stat in stats:
              print(str(stat) + ":" + str(stats[stat]))
  
file_path: pfrl/examples/atari/reproduction/rainbow/train_rainbow.py
imports: import argparse
  import json
  import os
  import numpy
  import torch
  import pfrl
  from pfrl import agents, experiments, explorers
  from pfrl import nn
  from pfrl import replay_buffers, utils
  from pfrl.q_functions import DistributionalDuelingDQN
  from pfrl.wrappers import atari_wrappers
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: env, unknown