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Reinforcement Learning: An Introduction
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TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.Abstract:
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.read more
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Human-level control through deep reinforcement learning
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Mastering the game of Go with deep neural networks and tree search
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
References
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Proceedings Article
On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains
Proceedings Article
Stable Fitted Reinforcement Learning
TL;DR: The reinforcement learning problem is described, algorithms which seek an approximation to the Q function are motivated, and new convergence results for two such algorithms are presented.
Journal ArticleDOI
Splines and efficiency in dynamic programming
TL;DR: It is shown how one can use splines, represented in the B-spline basis, to reduce the difficulties of large storage requirements in dynamic programming via approximations to the minimum-return function without the inefficiency associated with using polynomials to the same end.
Journal ArticleDOI
Improved Allocation of Weights for Associative Memory Storage in Learning Control Systems
P.C. Parks,J. Militzer +1 more
TL;DR: In this article, the problem of finding vectors a is shown to be equivalent to geometrical problem of the placing of ρ -1 points have differing integer coordinates inside a hypercube of dimension n and size ρ x ρ X ρ y ρx x y x y y y x x y X y y X X y x Y x y Y ρ repeated n times, and a table of such lattices is presented.
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Human-level control through deep reinforcement learning
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