evaluate_policy(model, env, n_eval_episodes=10, deterministic=True, render=False, callback=None, reward_threshold=None, return_episode_rewards=False)¶
Runs policy for n_eval_episodes episodes and returns average reward. This is made to work only with one env.
- 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 – (bool) If True, a list of reward per episode will be returned instead of the mean.
(float, float) Mean reward per episode, std of reward per episode returns ([float], [int]) when return_episode_rewards is True