code: def _test_abc(
      self,
      t_max,
      use_lstm,
      discrete=True,
      episodic=True,
      steps=100000,
      require_success=True,
  ):
      nproc = 8
  
      def make_env(process_idx, test):
          size = 2
          return ABC(
              size=size,
              discrete=discrete,
              episodic=episodic or test,
              partially_observable=self.use_lstm,
              deterministic=test,
          )
  
      sample_env = make_env(0, False)
      action_space = sample_env.action_space
      obs_space = sample_env.observation_space
  
      def phi(x):
          return x
  
      n_hidden_channels = 20
      n_hidden_layers = 1
      nonlinearity = F.leaky_relu
      replay_buffer = EpisodicReplayBuffer(10**4)
      if use_lstm:
          if discrete:
              model = acer.ACERSharedModel(
                  shared=L.LSTM(obs_space.low.size, n_hidden_channels),
                  pi=policies.FCSoftmaxPolicy(
                      n_hidden_channels,
                      action_space.n,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                      min_prob=1e-1,
                  ),
                  q=q_function.FCStateQFunctionWithDiscreteAction(
                      n_hidden_channels,
                      action_space.n,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
              )
          else:
              model = acer.ACERSDNSharedModel(
                  shared=L.LSTM(obs_space.low.size, n_hidden_channels),
                  pi=policies.FCGaussianPolicy(
                      n_hidden_channels,
                      action_space.low.size,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      bound_mean=True,
                      min_action=action_space.low,
                      max_action=action_space.high,
                      nonlinearity=nonlinearity,
                      min_var=1e-1,
                  ),
                  v=v_function.FCVFunction(
                      n_hidden_channels,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
                  adv=q_function.FCSAQFunction(
                      n_hidden_channels,
                      action_space.low.size,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
              )
      else:
          if discrete:
              model = acer.ACERSeparateModel(
                  pi=policies.FCSoftmaxPolicy(
                      obs_space.low.size,
                      action_space.n,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                      min_prob=1e-1,
                  ),
                  q=q_function.FCStateQFunctionWithDiscreteAction(
                      obs_space.low.size,
                      action_space.n,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
              )
          else:
              model = acer.ACERSDNSeparateModel(
                  pi=policies.FCGaussianPolicy(
                      obs_space.low.size,
                      action_space.low.size,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      bound_mean=True,
                      min_action=action_space.low,
                      max_action=action_space.high,
                      nonlinearity=nonlinearity,
                      min_var=1e-1,
                  ),
                  v=v_function.FCVFunction(
                      obs_space.low.size,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
                  adv=q_function.FCSAQFunction(
                      obs_space.low.size,
                      action_space.low.size,
                      n_hidden_channels=n_hidden_channels,
                      n_hidden_layers=n_hidden_layers,
                      nonlinearity=nonlinearity,
                  ),
              )
      eps = 1e-8
      opt = rmsprop_async.RMSpropAsync(lr=1e-3, eps=eps, alpha=0.99)
      opt.setup(model)
      gamma = 0.5
      beta = 1e-5
      if self.n_times_replay == 0 and self.disable_online_update:
          # At least one of them must be enabled
          return
      agent = acer.ACER(
          model,
          opt,
          replay_buffer=replay_buffer,
          t_max=t_max,
          gamma=gamma,
          beta=beta,
          phi=phi,
          n_times_replay=self.n_times_replay,
          act_deterministically=True,
          disable_online_update=self.disable_online_update,
          replay_start_size=100,
          use_trust_region=self.use_trust_region,
      )
  
      max_episode_len = None if episodic else 2
  
      with warnings.catch_warnings(record=True) as warns:
          train_agent_async(
              outdir=self.outdir,
              processes=nproc,
              make_env=make_env,
              agent=agent,
              steps=steps,
              max_episode_len=max_episode_len,
              eval_interval=500,
              eval_n_steps=None,
              eval_n_episodes=5,
              successful_score=1,
          )
          assert len(warns) == 0, warns[0]
  
      # The agent returned by train_agent_async is not guaranteed to be
      # successful because parameters could be modified by other processes
      # after success. Thus here the successful model is loaded explicitly.
      if require_success:
          agent.load(os.path.join(self.outdir, "successful"))
      agent.stop_episode()
  
      # Test
      env = make_env(0, True)
      n_test_runs = 5
  
      for _ in range(n_test_runs):
          total_r = 0
          obs = env.reset()
          done = False
          reward = 0.0
  
          while not done:
              action = agent.act(obs)
              print("state:", obs, "action:", action)
              obs, reward, done, _ = env.step(action)
              total_r += reward
          if require_success:
              self.assertAlmostEqual(total_r, 1)
          agent.stop_episode()
  
imports: import copy
  import logging
  import os
  import tempfile
  import unittest
  import warnings
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import testing
  from chainer.testing import condition
  import numpy
  import chainerrl
  from chainerrl.agents import acer
  from chainerrl.envs.abc import ABC
  from chainerrl.experiments.train_agent_async import train_agent_async
  from chainerrl.optimizers import rmsprop_async
  from chainerrl import policies
  from chainerrl import q_function
  from chainerrl.replay_buffer import EpisodicReplayBuffer
  from chainerrl import v_function
label_desc: Use image node_id 4 for calling the TestACER obj's underlying member method code with example usage: obj._test_abc(t_max, use_lstm, discrete, episodic, steps, require_success) and returns: ABC, x