Stats for nerds
1recommend
7mentions
3people
2022–2025years
71% recommend29% neutral
Mentions per year
What Hacker News says
- I second this suggestion. Read Grokking Deep Reinforcement Learning before reading Sutton. Well, the Sutton book is free, so take a peak, but if the formulas scare you then read Grokking Deep Reinforcement Learning.
- I enjoyed "Grokking Deep Reinforcement Learning"[0]. It doesn't include anything about transformers though. Also, see Python's gymnasium[1] library for a lunar lander environment, it's the one I focused on most while I was learning and I've solved it a few different ways now. You can also look at my own notebook I used when implementing Soft Actor Critic with PyTorch not too long ago[2], it's not great for teaching, but maybe you can get something out of it.
[0]: https://www.manning.com/books/grokking-deep-reinforcement-le... [1]: https://gymnasium.farama.org/environments/box2d/ [2]: https://github.com/DevJac/learn-pytorch/blob/main/SAC.ipynb
- This is a good and short introduction to RL. The density of the information in Spinning Up was just right for me and I think I've referred to it more often than any other resource when actually implementing my own RL algorithms (PPO and SAC).
If I had to recommend a curriculum to a friend I would say:
(1) Spend a few hours on Spinning Up.
(2) If the mathematical notation is intimidating, read Grokking Deep Reinforcement Learning (from Manning), which is slower paced and spends a lot of time explaining the notation itself, rather than just assuming the mathematical notation is self-explanatory as is so often the case. This book has good theoretical explanations and will get you some running code.
(3) Spend a few hours with Spinning Up again. By this point you should be a little comfortable with a few different RL algorithms.
(4) Read Sutton's book, which is "the bible" of reinforcement learning. It's quite approachable, but it would be a bit dry and abstract without some hands-on experience with RL I think.
- I enjoyed the book Grokking Deep Reinforcement Learning from Manning. It's written in a very accessible style and explains the mathematical formulas you will see in other RL teaching material.
I'd suggest getting a good book or other teaching resource and solve a few Gymnasium[0] environments. Unlike supervised machine learning, you don't need someone else's data, you generate your own data.
- Thirded. I also like Grokking Deep Reinforcement Learning a lot. OReilly's Grokking series is 2 for 2 for me. For those who don't know, OReilly stopped selling DRM free PDFs directly, but they still sell DRM free PDFs through other sources like ebooks.com, which I appreciate.
- "Grokking Deep Reinforcement Learning" by Miguel Morales and "Deep Reinforcement Learning in Action" by Alexander Zai and Brandon Brown both look promising, though the code might be outdated. Looks like they use the OpenAI Gym environment, which has since been forked and maintained as Gymnasium.
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