Kevin Murphy
2024 · arXiv
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods, model-based methods, multi-agent RL, LLMs and RL, and various other topics (e.g., offline RL, hierarchical RL, intrinsic reward). It also includes some code snippets for training LLMs with RL.
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> "Probabilistic Machine Learning" by Murphy [...] even if it contains virtually no deep learning in it This is confusing. Are you referring to the old 2012 version? Volumes 1 & 2 (2022-3) contain a substantial amount of deep learning [1], including relatively recent developments. There's also a new RL volume getting written, with some drafts deposited in arXiv [2]. [1] https://probml.github.io/pml-book [2] https://arxiv.org/pdf/2412.05265
Kindly suggest some books about RL? I've already studied a lot of deep learning. Please confirm if these resoruces are good, or suggest yours: Sutton et al. - Reinforcement Learning Kevin Patrick Murphy - Reinforcement Learning, an overview https://arxiv.org/abs/2412.05265 Sebastian Raschka (upcoming book) ...
Also worth mentioning Murphy's WIP textbook[0] focused entirely on RL, which is an outgrowth of his excellent ML textbooks. [0]: https://arxiv.org/abs/2412.05265
* 1 point by _giorgio_ 0 minutes ago | next | edit | delete [–] https://arxiv.org/abs/2412.05265 Reinforcement Learning: An Overview Kevin Murphy This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs). From: Kevin Murphy [view email] [v1] Fri, 6 Dec 2024 18:53:49 UTC (6,099 KB)
https://arxiv.org/abs/2412.05265 Reinforcement Learning: An Overview Kevin Murphy This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs). From: Kevin Murphy [view email] [v1] Fri, 6 Dec 2024 18:53:49 UTC (6,099 KB)
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