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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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Pattern Recognition and Machine Learning
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References
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Journal ArticleDOI
Synaptic tagging and long-term potentiation
Uwe Frey,Richard G. M. Morris +1 more
TL;DR: It is shown that weak tetanic stimulation, which ordinarily leads only to early LTP, or repeated tetanization in the presence of protein-Synthesis inhibitors, each results in protein-synthesis-dependent late LTP; this indicates that the persistence of LTP depends not only on local events during its induction, but also on the prior activity of the neuron.
Predictability, surprise, attention, and conditioning
TL;DR: The role of attention in Pavlovian conditioning, and use of auditory and visual stimuli to condition rats is discussed in this article, where the authors discuss the use of both visual and auditory stimuli.
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Neurons in the orbitofrontal cortex encode economic value
TL;DR: Neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods during economic choice, suggesting that economic choice is essentially choice between goods rather than choice between actions.
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Monte Carlo Strategies in Scientific Computing
TL;DR: The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.
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