The Online Books Page

Reinforcement learning

See also what's at Wikipedia, your library, or elsewhere.

• Machine learning
• Reinforcement (Psychology)

• Reinforcement Learning: An Introduction (first and second editions, 1998 and 2018), by Richard S. Sutton and Andrew G. Barto (HTML and PDF at incompleteideas.net)

Items below (if any) are from related and broader terms.

• Graph Representation Learning (prepublication version, 2020), by William L. Hamilton (PDF at McGill)
• Probabilistic Machine Learning for Civil Engineers (prepublication version; 2020), by James-A. Goulet (PDF files with commentary at polymtl.ca)
• Foundations of Machine Learning (second edition; Cambridge, MA and London: MIT Press, c2018), by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (HTML at ublish.com)
• Elements of Causal Inference: Foundations and Learning Algorithms (Cambridge, MA and London: MIT Press, c2017), by Jonas Peters, Dominik Janzing, and Bernhard Schölkopf (PDF with commentary at MIT Press)
• Machine Learning (main text c1997; additional chapters c2017), by Tom M. Mitchell (PDF with commentary at CMU)
• Deep Learning (prepublication version; 2016), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (illustrated HTML with commentary at deeplearningbook.org)
• Bayesian Reasoning and Machine Learning (free online edition, c2014), by David Barber (PDF with commentary in the UK)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (second edition, 2009), by Trevor Hastie, Robert Tibshirani, and J. H. Friedman (PDF with commentary at Stanford)
• Reinforcement Learning: An Introduction (first and second editions, 1998 and 2018), by Richard S. Sutton and Andrew G. Barto (HTML and PDF at incompleteideas.net)
• Pattern Recognition and Machine Learning (c2006), by Christopher M. Bishop (PDF at microsoft.com)
• Ethics and Data Science (c2019), by Michael Kosta Loukides, Hilary Mason, and DJ Patil (HTML at oreilly.com)
• A Course in Machine Learning, by Hal Daumé (PDF with commentary at ciml.info)
• Machine Learning, Neural and Statistical Classification, ed. by Donald Michie, D. J. Spiegelhalter, and Charles C. Taylor (PDF and gzipped Postscript in the UK)

• Gaussian Processes fo Machine Learning (online edition; originally published Cambridge, MA and London: MIT Press, c2006), by Carl Edward Rasmussen and Christopher K. I. Williams (PDF with commentary at gaussianprocess.org)

• Mathematics for Machine Learning (prepublication free online edition; 2020), by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (PDF with commentary at github.io)

• Journal of Machine Learning Research (2000-) (full serial archives)

• Learning Deep Architectures for AI (c2009), by Yoshua Bengio (PDF at Wayback Machine)

• Boosting: Foundations and Algorithms (Cambridge, MA and London: MIT Press, c2012), by Robert E. Schapire and Yoav Freund (HTML with commentary at MIT Press)

• Boosting: Foundations and Algorithms (Cambridge, MA and London: MIT Press, c2012), by Robert E. Schapire and Yoav Freund (HTML with commentary at MIT Press)

• Journal of Neurotherapy (partial serial archives)