# Copied from stable_baselines
import numpy as np
from stable_baselines3.common.vec_env import VecEnv
[docs]def 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.
:param model: (BaseAlgorithm) The RL agent you want to evaluate.
:param env: (gym.Env or VecEnv) The gym environment. In the case of a ``VecEnv``
this must contain only one environment.
:param n_eval_episodes: (int) Number of episode to evaluate the agent
:param deterministic: (bool) Whether to use deterministic or stochastic actions
:param render: (bool) Whether to render the environment or not
:param callback: (callable) callback function to do additional checks,
called after each step.
:param reward_threshold: (float) Minimum expected reward per episode,
this will raise an error if the performance is not met
:param return_episode_rewards: (bool) If True, a list of reward per episode
will be returned instead of the mean.
:return: (float, float) Mean reward per episode, std of reward per episode
returns ([float], [int]) when ``return_episode_rewards`` is True
"""
if isinstance(env, VecEnv):
assert env.num_envs == 1, "You must pass only one environment when using this function"
episode_rewards, episode_lengths = [], []
for _ in range(n_eval_episodes):
obs = env.reset()
done, state = False, None
episode_reward = 0.0
episode_length = 0
while not done:
action, state = model.predict(obs, state=state, deterministic=deterministic)
obs, reward, done, _info = env.step(action)
episode_reward += reward
if callback is not None:
callback(locals(), globals())
episode_length += 1
if render:
env.render()
episode_rewards.append(episode_reward)
episode_lengths.append(episode_length)
mean_reward = np.mean(episode_rewards)
std_reward = np.std(episode_rewards)
if reward_threshold is not None:
assert mean_reward > reward_threshold, "Mean reward below threshold: " f"{mean_reward:.2f} < {reward_threshold:.2f}"
if return_episode_rewards:
return episode_rewards, episode_lengths
return mean_reward, std_reward