Open AccessBook
Reinforcement Learning: An Introduction
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
Citations
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Journal ArticleDOI
The role of the striatum in social behavior.
TL;DR: The picture that emerges from this review is that the striatum is a general-purpose subcortical region capable of integrating social information into coding of social action and reward.
Proceedings Article
Neural Episodic Control
Alexander Pritzel,Benigno Uria,Sriram Srinivasan,Adrià Puigdomènech Badia,Oriol Vinyals,Demis Hassabis,Daan Wierstra,Charles Blundell +7 more
TL;DR: This work proposes Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them, and shows across a wide range of environments that the agent learns significantly faster than other state-of-the-art, general purpose deep reinforcementlearning agents.
Journal ArticleDOI
Toward a theory of generalization and learning in XCS
TL;DR: This work starts from Wilson's generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers, and derives a simple equation that supports the hypothesis theoretically.
Journal ArticleDOI
Online learning: A comprehensive survey
TL;DR: Online learning as mentioned in this paper is a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time.
Journal ArticleDOI
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
Yasar Sinan Nasir,Dongning Guo +1 more
TL;DR: The proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents and is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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