old_contents: import pytest
  import gym
  
  from baselines.run import get_learn_function
  from baselines.common.tests.util import reward_per_episode_test
  
  common_kwargs = dict(
      total_timesteps=30000,
      network='mlp',
      gamma=1.0,
      seed=0,
  )
  
  learn_kwargs = {
      'a2c' : dict(nsteps=32, value_network='copy', lr=0.05),
      'acktr': dict(nsteps=32, value_network='copy'),
      'deepq': dict(total_timesteps=20000),
      'ppo2': dict(value_network='copy'),
      'trpo_mpi': {}
  }
  
  @pytest.mark.slow
  @pytest.mark.parametrize("alg", learn_kwargs.keys())
  def test_cartpole(alg):
      '''
      Test if the algorithm (with an mlp policy)
      can learn to balance the cartpole
      '''
  
      kwargs = common_kwargs.copy()
      kwargs.update(learn_kwargs[alg])
  
      learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
      def env_fn():
  
          env = gym.make('CartPole-v0')
          env.seed(0)
          return env
  
      reward_per_episode_test(env_fn, learn_fn, 100)
  
  if __name__ == '__main__':
      test_cartpole('deepq')
  
new_contents: import pytest
  import gym
  
  from baselines.run import get_learn_function
  from baselines.common.tests.util import reward_per_episode_test
  
  common_kwargs = dict(
      total_timesteps=30000,
      network='mlp',
      gamma=1.0,
      seed=0,
  )
  
  learn_kwargs = {
      'a2c' : dict(nsteps=32, value_network='copy', lr=0.05),
      'acktr': dict(nsteps=32, value_network='copy', is_async=False),
      'deepq': dict(total_timesteps=20000),
      'ppo2': dict(value_network='copy'),
      'trpo_mpi': {}
  }
  
  @pytest.mark.slow
  @pytest.mark.parametrize("alg", learn_kwargs.keys())
  def test_cartpole(alg):
      '''
      Test if the algorithm (with an mlp policy)
      can learn to balance the cartpole
      '''
  
      kwargs = common_kwargs.copy()
      kwargs.update(learn_kwargs[alg])
  
      learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
      def env_fn():
  
          env = gym.make('CartPole-v0')
          env.seed(0)
          return env
  
      reward_per_episode_test(env_fn, learn_fn, 100)
  
  if __name__ == '__main__':
      test_cartpole('deepq')
  
subject: disable async acktr (#129)