DeepReinforcementLearning:深度RL实施。 在pytorch中实现的DQN,SAC,DDPG,TD3,PPO和VPG。 经过测试的环境:LunarLander-v2和Pendulum-v0-源码

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使用Pytorch实现的深度RL算法 算法列表: 关于深入探讨 实验结果: 算法 离散环境:LunarLander-v2 连续环境:Pendulum-v0 DQN -- VPG -- DDPG -- TD3 -- SAC -- PPO -- 用法: 只需直接运行文件/算法。 在我学习算法时,它们之间没有通用的结构。 不同的算法来自不同的来源。 资源: 未来的项目: 如果有时间,我将为使用RL的电梯添加一个简单的程序。 更好的图形

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