51 lines
4.5 KiB
Markdown
51 lines
4.5 KiB
Markdown
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[Eng](https://github.com/JohnJim0816/reinforcement-learning-tutorials/blob/master/README_en.md)|[中文](https://github.com/JohnJim0816/reinforcement-learning-tutorials/blob/master/README.md)
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## 写在前面
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本项目用于学习RL基础算法，尽量做到: **注释详细**，**结构清晰**。
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代码结构主要分为以下几个脚本：
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* ```model.py``` 强化学习算法的基本模型，比如神经网络，actor，critic等
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* ```memory.py``` 保存Replay Buffer，用于off-policy
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* ```plot.py``` 利用matplotlib或seaborn绘制rewards图，包括滑动平均的reward，结果保存在result文件夹中
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* ```env.py``` 用于构建强化学习环境，也可以重新自定义环境，比如给action加noise
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* ```agent.py``` RL核心算法，比如dqn等，主要包含update和choose_action两个方法，
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* ```main.py``` 运行主函数
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其中```model.py```,```memory.py```,```plot.py``` 由于不同算法都会用到，所以放入```common```文件夹中。
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## 运行环境
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python 3.7、pytorch 1.6.0-1.7.1、gym 0.17.0-0.18.0
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## 使用说明
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运行带有```train```的py文件或ipynb文件进行训练，如果前面带有```task```如```task0_train.py```，表示对task0任务训练
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类似的带有```eval```即为测试。
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## 算法进度
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| 算法名称 | 相关论文材料 | 环境 | 备注 |
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| :--------------------------------------: | :----------------------------------------------------------: | ----------------------------------------- | :--------------------------------: |
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| [On-Policy First-Visit MC](./MonteCarlo) | [medium blog](https://medium.com/analytics-vidhya/monte-carlo-methods-in-reinforcement-learning-part-1-on-policy-methods-1f004d59686a) | [Racetrack](./envs/racetrack_env.md) | |
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| [Q-Learning](./QLearning) | [towardsdatascience blog](https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56),[q learning paper](https://ieeexplore.ieee.org/document/8836506) | [CliffWalking-v0](./envs/gym_info.md) | |
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| [Sarsa](./Sarsa) | [geeksforgeeks blog](https://www.geeksforgeeks.org/sarsa-reinforcement-learning/) | [Racetrack](./envs/racetrack_env.md) | |
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| [DQN](./DQN) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf),[Nature DQN Paper](https://www.nature.com/articles/nature14236) | [CartPole-v0](./envs/gym_info.md) | |
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| [DQN-cnn](./DQN_cnn) | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [CartPole-v0](./envs/gym_info.md) | 与DQN相比使用了CNN而不是全链接网络 |
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| [DoubleDQN](./DoubleDQN) | [DoubleDQN Paper](https://arxiv.org/abs/1509.06461) | [CartPole-v0](./envs/gym_info.md) | |
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| [Hierarchical DQN](HierarchicalDQN) | [H-DQN Paper](https://arxiv.org/abs/1604.06057) | [CartPole-v0](./envs/gym_info.md) | |
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| [PolicyGradient](./PolicyGradient) | [Lil'log](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html) | [CartPole-v0](./envs/gym_info.md) | |
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| [A2C](./A2C) | [A3C Paper](https://arxiv.org/abs/1602.01783) | [CartPole-v0](./envs/gym_info.md) | |
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| [SAC](./SAC) | [SAC Paper](https://arxiv.org/abs/1801.01290) | [Pendulum-v0](./envs/gym_info.md) | |
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| [PPO](./PPO) | [PPO paper](https://arxiv.org/abs/1707.06347) | [CartPole-v0](./envs/gym_info.md) | |
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| [DDPG](./DDPG) | [DDPG Paper](https://arxiv.org/abs/1509.02971) | [Pendulum-v0](./envs/gym_info.md) | |
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| [TD3](./TD3) | [TD3 Paper](https://arxiv.org/abs/1802.09477) | [HalfCheetah-v2]((./envs/mujoco_info.md)) | |
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## Refs
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[RL-Adventure-2](https://github.com/higgsfield/RL-Adventure-2)
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[RL-Adventure](https://github.com/higgsfield/RL-Adventure) |