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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
Citations
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Journal ArticleDOI
Value-based deep reinforcement learning for adaptive isolated intersection signal control
Chia-Hao Wan,Ming-Chorng Hwang +1 more
TL;DR: A dynamic discount factor embedded in the iterative Bellman equation is proposed to prevent from a biased estimation of action-value function due to the effects of inconstant time step interval and shows that the trained agent outperforms a fixed timing plan in all testing cases with reducing system total delay by 20%.
Journal ArticleDOI
Individual learning of coordination knowledge
Sandip Sen,Mahendra Sekaran +1 more
TL;DR: This work evaluates individual and concurrent learning by multiple, autonomous agents as a means for acquiring coordination knowledge and shows that a uniform reinforcer for coordination schemes is needed.
Journal ArticleDOI
Learning and choosing in an uncertain world: An investigation of the explore-exploit dilemma in static and dynamic environments
TL;DR: While people often employ suboptimal strategies the first time they encounter the learning problem, most people are able to approximate the correct strategy after minimal experience and show a striking agreement between human behavior and the optimal policy.
Journal ArticleDOI
Transferring knowledge as heuristics in reinforcement learning
Reinaldo A. C. Bianchi,Luiz A. Celiberto,Paulo E. Santos,Jackson P. Matsuura,Ramon López de Mántaras +4 more
TL;DR: The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another domain ( the source domain).
Proceedings ArticleDOI
Fuzzy Q-learning for generalization of reinforcement learning
TL;DR: GARIC-Q is introduced, a new method for doing incremental dynamic programming using a society of intelligent agents which are controlled at the top level by fuzzy Q-learning and at the local level, each agent learns and operates based on GARIC.
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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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.