text: #! /usr/bin/env python
  
  import gym
  
  from stable_baselines3.common.evaluation import evaluate_policy
  from stable_baselines3.ppo.policies import MlpPolicy
  from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize, VecFrameStack
  from stable_baselines3 import A2C, SAC, PPO, TD3
  
  
  checkpoints_dir = "checkpoint" 
  env = gym.make('Pendulum-v0')
  env = DummyVecEnv([lambda: env])
  
  loaded_model = PPO.load(checkpoints_dir + "/PPO2_Pendulum")
  # print("loaded:", "gamma =", loaded_model.gamma, "n_steps =", loaded_model.n_steps)
  
  mean_reward, std_reward = evaluate_policy(loaded_model, env, n_eval_episodes=100)
  print(f"mean_reward:{mean_reward:.2f} +/- {std_reward:.2f}")
  
  loaded_model.set_env(DummyVecEnv([lambda: gym.make('Pendulum-v0')]))
  loaded_model.learn(8000)