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Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
| Date | Stars |
|---|---|
| 2026-07-24 | 1029 |
| 2026-07-25 | 1029 |
| 2026-07-28 | 1029 |
| 2026-07-30 | 1029 |
| 2026-07-31 | 1029 |
| 2026-08-04 | 1030 |
| 2026-08-06 | 1030 |
Today
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growth rate 0.10%/day
# Reinforcement Learning: Theory and Python Implementation **The First Reinforcement Learning Tutorial Book with one-on-one mapping TensorFlow 2 and PyTorch 1&2 Implementation** | [English Edition](https://github.com/ZhiqingXiao/rl-book/tree/master/en2024) | [中文版](https://github.com/ZhiqingXiao/rl-book/tree/master/zh2023) | [中文2019版](https://github.com/ZhiqingXiao/rl-book/tree/master/zh2019) | | :---: | :---: | :---: | | [](https://github.com/ZhiqingXiao/rl-book/tree/master/en2024) | [](https://github.com/ZhiqingXiao/rl-book/tree/master/zh2023) | [](https://github.com/ZhiqingXiao/rl-book/tree/master/zh2019) | Please email me if you are interested in publishing this book in other languages. **Features** This is a tutorial book on reinforcement learning, with explanation of theory and Python implementation. - Theory: Starting from a uniform mathematical framework, this book derives the theory and algorithms of reinforcement learning, including the algorithms in large model era such as PPO, RLHF, IRL, and PbRL. - Practice: Every chapter is accompanied by high quality implementation based on Python 3, Gym 0.26, and TensorFlow 2 / PyTorch 1&2. All codes are compatible with Windows, Linux, and macOS, can be run in a laptop. ### Supporting contents for English version Check [here](https://github.com/ZhiqingXiao/rl-book/tree/master/en2024) for codes, exercise answers, errata, etc. Check [SpringerLink](https://doi.org/10.1007/978-981-19-4933-3) [Amazon](https://www.amazon.com/dp/9811949328) for book contents. ### Table of Codes All codes have been saved as a .ipynb file and a .html file in the same directory. | Chapter | Environment & Closed-Form Policy | Agent | | :--- | :--- | :--- | | 2 | [CliffWalking-v0](https://zhiqingxiao.github.io/rl-book/en2024/code/CliffWalking-v0_ClosedForm.html) | [Bellman](https://zhiqingxiao.github.io/rl-book/en2024/code/CliffWalking-v0_Bellman_demo.html) | | 3 | [FrozenLake-v1](https://zhiqingxiao.github.io/rl-book/en2024/code/FrozenLake-v1_ClosedForm.html)| [DP](https://zhiqingxiao.github.io/rl-book/en2024/code/FrozenLake-v1_DP_demo.html) | | 4 | [Blackjack-v1](https://zhiqingxiao.github.io/rl-book/en2024/code/Blackjack-v1_ClosedForm.html) | [MC](https://zhiqingxiao.github.io/rl-book/en2024/code/Blackjack-v1_MonteCarlo_demo.html) | | 5 | [Taxi-v3](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_ClosedForm.html) | [SARSA](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_SARSA_demo.html), [ExpectedSARSA](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_ExpectedSARSA.html), [QL](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_QLearning.html), [DoubleQL](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_DoubleQLearning.html), [SARSA(λ)](https://zhiqingxiao.github.io/rl-book/en2024/code/Taxi-v3_SARSALambda.html) | | 6 | [MountainCar-v0](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_ClosedForm.html) | [SARSA](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_SARSA.html), [SARSA(λ)](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_SARSAlambda.html), DQN [tf](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DQN_tf.html) [torch](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DQN_torch.html), DoubleDQN [tf](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DoubleDQN_tf.html) [torch](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DoubleDQN_torch.html), DuelDQN [tf](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DuelDQN_tf.html) [torch](https://zhiqingxiao.github.io/rl-book/en2024/code/MountainCar-v0_DuelDQN_torch.html) | | 7 | [CartPole-0](https://zhiqingxiao.github.io/rl-book/en2024/code/CartPole-v0_ClosedForm.html) | VPG [tf
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Zhiqing Xiao · Tsinghua University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:49b63f44be1799a4, topic:reinforcement-learning, topic:deep-reinforcement-learning, topic:openai-gym
matched fp:49b63f44be1799a4, topic:pytorch, topic:tensorflow