强化学习：

代码学习网站：

教程：https://stable-baselines.readthedocs.io/en/master/guide/examples.html

gym使用

在做rl时候 ，如何利用gym将动画动起来，让每一步训练过程可视化：

例程：

import gym

from stable_baselines import DQN
from stable_baselines.common.evaluation import evaluate_policy


# Create environment
env = gym.make('LunarLander-v2')

# Instantiate the agent
model = DQN('MlpPolicy', env, learning_rate=1e-3, prioritized_replay=True, verbose=1)
# Train the agent
model.learn(total_timesteps=int(2e5))
# Save the agent
model.save("dqn_lunar")
del model  # delete trained model to demonstrate loading

# Load the trained agent
model = DQN.load("dqn_lunar")

# Evaluate the agent
mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)

# Enjoy trained agent
obs = env.reset()
for i in range(1000):
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()

from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.vec_env import VecFrameStack
from stable_baselines import ACER

# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multiprocessing training (num_env=4 => 4 processes)
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)

model = ACER('CnnPolicy', env, verbose=1)
model.learn(total_timesteps=25000)

obs = env.reset()
while True:
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()

bug解决：

在执行时：

import gym
env = gym.make('ALE/Pong-v5')
env.reset()

for i in range(1000):
    env.step(env.action_space.sample())
    env.render()
env.close()

输出，无法对fream进行渲染。

ImportError: cannot import name ‘rendering’ from ‘gym.envs.classic_control’.

解决办法：

打开 包gym.envs.classic_control，发现没有 rendering .py文件，去github，发现，main分支确实已经没有这个文件了，应该是版本的问题，最新版本已经去掉了该文件，然而其他分支是有的，所以将该文件下载并放在包对应位置。

此外，还需要 在代码中加入

from gym.envs.classic_control import rendering

导入rendering.py