code: def main():
      parser = argparse.ArgumentParser(
          description="Chainer example: DDPG"
      )
      parser.add_argument(
          "--env",
          type=str,
          default="Pendulum-v0",
          help="Name of the OpenAI Gym environment",
      )
      parser.add_argument(
          "--batch-size",
          "-b",
          type=int,
          default=64,
          help="Number of transitions in each mini-batch",
      )
      parser.add_argument(
          "--episodes",
          "-e",
          type=int,
          default=1000,
          help="Number of episodes to run",
      )
      parser.add_argument(
          "--device",
          "-d",
          type=str,
          default="-1",
          help="Device specifier. Either ChainerX device "
          "specifier or an integer. If non-negative integer, "
          "CuPy arrays with specified device id are used. If "
          "negative integer, NumPy arrays are used",
      )
      parser.add_argument(
          "--out",
          "-o",
          default="ddpg_result",
          help="Directory to output the result",
      )
      parser.add_argument(
          "--unit", "-u", type=int, default=100, help="Number of units"
      )
      parser.add_argument(
          "--reward-scale",
          type=float,
          default=1e-3,
          help="Reward scale factor",
      )
      parser.add_argument(
          "--replay-start-size",
          type=int,
          default=500,
          help=(
              "Number of iterations after which replay is " "started"
          ),
      )
      parser.add_argument(
          "--tau",
          type=float,
          default=1e-2,
          help="Softness of soft target update (0, 1]",
      )
      parser.add_argument(
          "--noise-scale",
          type=float,
          default=0.4,
          help="Scale of additive Gaussian noises",
      )
      parser.add_argument(
          "--record",
          action="store_true",
          default=True,
          help="Record performance",
      )
      parser.add_argument(
          "--no-record", action="store_false", dest="record"
      )
      group = parser.add_argument_group("deprecated arguments")
      group.add_argument(
          "--gpu",
          "-g",
          dest="device",
          type=int,
          nargs="?",
          const=0,
          help="GPU ID (negative value indicates CPU)",
      )
      args = parser.parse_args()
  
      if chainer.get_dtype() == np.float16:
          warnings.warn(
              "This example may cause NaN in FP16 mode.", RuntimeWarning
          )
  
      device = chainer.get_device(args.device)
      device.use()
  
      # Initialize an environment
      env = gym.make(args.env)
      assert isinstance(env.observation_space, gym.spaces.Box)
      assert isinstance(env.action_space, gym.spaces.Box)
      obs_size = env.observation_space.low.size
      action_size = env.action_space.low.size
      if args.record:
          env = gym.wrappers.Monitor(env, args.out, force=True)
      reward_threshold = env.spec.reward_threshold
      if reward_threshold is not None:
          print(
              '{} defines "solving" as getting average reward of {} over 100 '
              "consecutive trials.".format(args.env, reward_threshold)
          )
      else:
          print(
              "{} is an unsolved environment, which means it does not have a "
              "specified reward threshold at which it's considered "
              "solved.".format(args.env)
          )
  
      # Initialize variables
      D = collections.deque(maxlen=10**6)  # Replay buffer
      Rs = collections.deque(maxlen=100)  # History of returns
      iteration = 0
  
      # Initialize models and optimizers
      Q = QFunction(obs_size, action_size, n_units=args.unit)
      policy = Policy(
          obs_size,
          action_size,
          env.action_space.low,
          env.action_space.high,
          n_units=args.unit,
      )
      Q.to_device(device)
      policy.to_device(device)
      target_Q = copy.deepcopy(Q)
      target_policy = copy.deepcopy(policy)
      opt_Q = optimizers.Adam(
          eps=1e-5
      )  # Use larger eps in case of FP16 mode
      opt_Q.setup(Q)
      opt_policy = optimizers.Adam(alpha=1e-4)
      opt_policy.setup(policy)
  
      for episode in range(args.episodes):
          obs = env.reset()
          done = False
          R = 0.0  # Return (sum of rewards obtained in an episode)
          timestep = 0
  
          while not done and timestep < env.spec.timestep_limit:
              # Select an action with additive noises for exploration
              action = get_action(policy, obs) + np.random.normal(
                  scale=args.noise_scale
              )
  
              # Execute an action
              new_obs, reward, done, _ = env.step(
                  np.clip(
                      action,
                      env.action_space.low,
                      env.action_space.high,
                  )
              )
              R += reward
  
              # Store a transition
              D.append(
                  (
                      obs,
                      action,
                      reward * args.reward_scale,
                      done,
                      new_obs,
                  )
              )
              obs = new_obs
  
              # Sample a random minibatch of transitions and replay
              if len(D) >= args.replay_start_size:
                  sample_indices = random.sample(
                      range(len(D)), args.batch_size
                  )
                  samples = [D[i] for i in sample_indices]
                  update(
                      Q,
                      target_Q,
                      policy,
                      target_policy,
                      opt_Q,
                      opt_policy,
                      samples,
                  )
  
              # Soft update of the target networks
              soft_copy_params(Q, target_Q, args.tau)
              soft_copy_params(policy, target_policy, args.tau)
  
              iteration += 1
              timestep += 1
  
          Rs.append(R)
          average_R = np.mean(Rs)
          print(
              "episode: {} iteration: {} R:{} average_R:{}".format(
                  episode, iteration, R, average_R
              )
          )
  
          if (
              reward_threshold is not None
              and average_R >= reward_threshold
          ):
              print(
                  "Solved {} by getting average reward of "
                  "{} >= {} over 100 consecutive episodes.".format(
                      args.env, average_R, reward_threshold
                  )
              )
              break
  
imports: from __future__ import division
  import argparse
  import collections
  import copy
  import random
  import warnings
  import gym
  import numpy
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import optimizers
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types