class_docstr: [Arcade Learning Environment](https://github.com/mgbellemare/Arcade-Learning-Environment)
  adapter (specification key: `ale`, `arcade_learning_environment`).
  
  May require:
  ```bash
  sudo apt-get install libsdl1.2-dev libsdl-gfx1.2-dev libsdl-image1.2-dev cmake
  ```
  
  Args:
      level (string): ALE rom file
          (<span style="color:#C00000"><b>required</b></span>).
      loss_of_life_termination: Signals a terminal state on loss of life
          (<span style="color:#00C000"><b>default</b></span>: false).
      loss_of_life_reward (float): Reward/Penalty on loss of life (negative values are a penalty)
          (<span style="color:#00C000"><b>default</b></span>: 0.0).
      repeat_action_probability (float): Repeats last action with given probability
          (<span style="color:#00C000"><b>default</b></span>: 0.0).
      visualize (bool): Whether to visualize interaction
          (<span style="color:#00C000"><b>default</b></span>: false).
      frame_skip (int > 0): Number of times to repeat an action without observing
          (<span style="color:#00C000"><b>default</b></span>: 1).
      seed (int): Random seed
          (<span style="color:#00C000"><b>default</b></span>: none).
code: def __init__(
      self,
      level,
      life_loss_terminal=False,
      life_loss_punishment=0.0,
      repeat_action_probability=0.0,
      visualize=False,
      frame_skip=1,
      seed=None,
  ):
      super().__init__()
  
      from ale_py import ALEInterface
  
      self.environment = ALEInterface()
      self.rom_file = level
  
      self.life_loss_terminal = life_loss_terminal
      self.life_loss_punishment = life_loss_punishment
  
      self.environment.setFloat(
          b"repeat_action_probability", repeat_action_probability
      )
      self.environment.setBool(b"display_screen", visualize)
      self.environment.setInt(b"frame_skip", frame_skip)
      if seed is not None:
          self.environment.setInt(b"random_seed", seed)
  
      # All set commands must be done before loading the ROM.
      self.environment.loadROM(self.rom_file.encode())
      self.available_actions = tuple(
          self.environment.getLegalActionSet()
      )
  
file_path: tensorforce/tensorforce/environments/arcade_learning_environment.py
imports: import numpy
  from tensorforce.environments import Environment
label_desc: Use image node_id 1 to create a new ArcadeLearningEnvironment object from inherited base classes: Environment with example: obj = ArcadeLearningEnvironment(level, life_loss_terminal, life_loss_punishment, repeat_action_probability, visualize, frame_skip, seed)