Evaluation Helper

stable_baselines.common.evaluation.evaluate_policy(model: BaseRLModel, env: Union[gym.core.Env, stable_baselines.common.vec_env.base_vec_env.VecEnv], n_eval_episodes: int = 10, deterministic: bool = True, render: bool = False, callback: Optional[Callable] = None, reward_threshold: Optional[float] = None, return_episode_rewards: bool = False) → Union[Tuple[float, float], Tuple[List[float], List[int]]][source]

Runs policy for n_eval_episodes episodes and returns average reward. This is made to work only with one env.

Parameters:
  • model – (BaseRLModel) The RL agent you want to evaluate.
  • env – (gym.Env or VecEnv) The gym environment. In the case of a VecEnv this must contain only one environment.
  • n_eval_episodes – (int) Number of episode to evaluate the agent
  • deterministic – (bool) Whether to use deterministic or stochastic actions
  • render – (bool) Whether to render the environment or not
  • callback – (callable) callback function to do additional checks, called after each step.
  • reward_threshold – (float) Minimum expected reward per episode, this will raise an error if the performance is not met
  • return_episode_rewards – (Optional[float]) If True, a list of reward per episode will be returned instead of the mean.
Returns:

(float, float) Mean reward per episode, std of reward per episode returns ([float], [int]) when return_episode_rewards is True