强化学习示例：乒乓球ALE/Pong

import gymnasium as gym
import numpy as np
from stable_baselines3 import DQN
from stable_baselines3.dqn import CnnPolicy

game="ALE/Pong-v5"
env=gym.make(game,render_mode="rgb_array")

save_file="dqn_"+game
print(env.action_space)
print(env.unwrapped.get_action_meanings())

model=DQN(CnnPolicy,env,verbose=1,exploration_final_eps=0.01,exploration_fraction=0.1,gradient_steps=1,learning_rate=0.0001,buffer_size=10000)
model.set_env(env)
model.learn(total_timesteps=1000000,log_interval=10)
model.save(save_file)

observation,info=env.reset()
score=0
rewards_sum=0

while True:
    action,_states=model.predict(observation,deterministic=True)
    observation,reward,terminated,truncated,info=env.step(env)
    score=score+1
    rewards_sum+=reward
    if reward > 0:
        print("Win!!!!",reward)
    if terminated or truncated:
        print("Finished ",score)
        print("Reward sum=",rewards_sum)
        break

上述代码在运行的时候，需要安装几个依赖包，具体如下：
pip install gymnasium[atari]
pip install gymnasium[accept-rom-license]
pip install stable_baselines3

而且运行的时候建议使用GPU，否则Model重新训练的过程会很漫长。