References

Reinforcement Learning¹

Reinforcement Learning: An Introduction (Richard S. Sutton, Andrew G. Barto) [1998][2018]

Algorithms for Reinforcement Learning (Csaba Szepesvari)

Dynamic Programming and Optimal Control (Chapter 6 - online) (Dimitri P. Bertsekas)

Glossary of Terminology in Reinforcement Learning

Evolution Strategy

Terms and Definitions

An Introduction to Evolution Strategy (Nikolaus Hansen)

Dr. P. N. Suganthan (code)

Supervised Learning²

Proceedings

JMLR [proceedings][papers][software]

ICML

NIPS

RSS

PMLR

Statistics

The Elements of Statistical Learning (T. Hastie, R. Tibshirani, J. Friedman)

Artificial Intelligence

Artificial Intelligence - Foundations of Computational Agents (David Poole, Alan Mackworth) [2nd edition - full text]

Thinking in C++ [Vol.1][Vol.2]

¹"Reinforcement learning is learning what to do --- how to map situations to actions --- so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."

-Reinforcement learning: An Introduction - (1.1)

All reinforcement learning methods have to use learning by selection in one form or another and this contrasts sharply with supervised learning, where the feedback from the environment directly indicates what the correct action should have been.

-Reinforcement learning: An Introduction - (2.4)

²"It's not who has the best algorithm that wins, it's who has the most data."