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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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Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
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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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Journal ArticleDOI
Shaping robot behavior using principles from instrumental conditioning
TL;DR: A computational model of this shaping process and its implementation on a mobile robot is described, which allows an RWI B21 robot to learn several distinct tasks derived from the same innate behavior.
Proceedings Article
Temporal Difference Learning in Continuous Time and Space
TL;DR: A continuous-time, continuous-state version of the temporal difference algorithm is derived in order to facilitate the application of reinforcement learning to real-world control tasks and neurobiological modeling.
Journal ArticleDOI
Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: response topography, neuronal firing, and interstimulus intervals.
John W. Moore,John E. Desmond,Neil E. Berthier,Diana E.J. Blazis,Richard S. Sutton,Andrew G. Barto +5 more
TL;DR: The model successfully simulates the aforementioned features of NM response topography and is capable of simulating appropriate ISI functions, i.e. with maximum conditioning strength with ISIs of 250 ms, for forward-delay and trace conditioning paradigms.
Journal ArticleDOI
A dynamic channel assignment policy through Q-learning
Junhong Nie,Simon Haykin +1 more
TL;DR: A novel approach to solving the dynamic channel assignment (DCA) problem by using a form of realtime reinforcement learning known as Q-learning in conjunction with neural network representation, capable of achieving a performance similar to that achieved by the MAXIAVIAL, but with a significantly reduced computational complexity.
Proceedings ArticleDOI
Minimum-time control of the Acrobot
TL;DR: A direct search algorithm for finding swingup trajectories for the Acrobot is described, which uses a lookahead search that maximizes theAcrobot's total energy in an N-step window.
Related Papers (5)
Human-level control through deep reinforcement learning
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more