Evaluation Helper

stable_baselines3.common.evaluation.evaluate_policy(model, env, n_eval_episodes=10, deterministic=True, render=False, callback=None, reward_threshold=None, return_episode_rewards=False)[source]

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

Parameters

  • model – (BaseAlgorithm) 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 – (bool) 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