code: import chainer
  import numpy as np
  from chainerrl.agents import a3c
  from chainerrl import links
  from chainerrl import misc
  from chainerrl.optimizers import rmsprop_async
  from chainerrl import policy
  from chainerrl import v_function
  
  from chainerrl.wrappers import atari_wrappers
  
  from chainerrl_visualizer import launch_visualizer
  
  
  class A3CFF(chainer.ChainList, a3c.A3CModel):
  
      def __init__(self, n_actions):
          self.head = links.NIPSDQNHead()
          self.pi = policy.FCSoftmaxPolicy(
              self.head.n_output_channels, n_actions)
          self.v = v_function.FCVFunction(self.head.n_output_channels)
          super().__init__(self.head, self.pi, self.v)
  
      def pi_and_v(self, state):
          out = self.head(state)
          return self.pi(out), self.v(out)
  
  
  def phi(x):
      # Feature extractor
      return np.asarray(x, dtype=np.float32) / 255
  
  
  def make_env():
      env = atari_wrappers.wrap_deepmind(
          atari_wrappers.make_atari(env_name),
          episode_life=False,
          clip_rewards=False)
      env.seed(seed)
      return env
  
  
  seed = 0
  env_name = 'BreakoutNoFrameskip-v4'
  
  misc.set_random_seed(seed)
  
  env = make_env()
  n_actions = env.action_space.n
  
  model = A3CFF(n_actions)
  opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99)
  opt.setup(model)
  opt.add_hook(chainer.optimizer.GradientClipping(40))
  
  agent = a3c.A3C(model, opt, t_max=5, gamma=0.99,
                  beta=1e-2, phi=phi)
  
  agent.load('parameters')
  
  ACTION_MEANINGS = {
      0: 'NOOP',
      1: 'FIRE',
      2: 'RIGHT',
      3: 'LEFT',
  }
  
  launch_visualizer(agent, env, ACTION_MEANINGS, raw_image_input=True)
  
apis: numpy.asarray