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Reinforcement Learning: A Survey
TLDR
A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.Abstract:
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.read more
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Proceedings Article
Friend-or-Foe Q-learning in General-Sum Games
TL;DR: This paper describes an approach to reinforcement learning in multiagent general-sum games in which a learner is told to treat each other agent as either a "friend" or "foe" in a Q-learning-style algorithm.
Journal ArticleDOI
Probabilistic Algorithms in Robotics
TL;DR: It is proposed that the probabilistic approach to robotics scales better to complex real-world applications than approaches that ignore a robot's uncertainty.
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Planning, Learning and Coordination in Multiagent Decision Processes
TL;DR: The extent to which methods from single-agent planning and learning can be applied in multiagent settings is investigated and the decomposition of sequential decision processes so that coordination can be learned locally, at the level of individual states.
Proceedings Article
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
Amy McGovern,Andrew G. Barto +1 more
TL;DR: This paper presents a method by which a reinforcement learning agent can automatically discover certain types of subgoals online and is able to accelerate learning on the current task and to transfer its expertise to other, related tasks through the reuse of its ability to attainSubgoals.
Proceedings Article
A Bayesian Framework for Reinforcement Learning
TL;DR: It is proposed that the learning process estimates online the full posterior distribution over models and to determine behavior, a hypothesis is sampled from this distribution and the greedy policy with respect to the hypothesis is obtained by dynamic programming.
References
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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.
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
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Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
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Parallel and Distributed Computation: Numerical Methods
TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.