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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
Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks
Wanlu Lei,Yu Ye,Ming Xiao +2 more
TL;DR: A framework based on deep reinforcement learning (DRL) is developed to solve the spectrum allocation problem in the emerging integrated access and backhaul (IAB) architecture with large scale deployment and dynamic environment by integrating an actor-critic spectrum allocation (ACSA) scheme and deep neural network (DNN) to achieve real-time spectrum allocation in different scenarios.
Proceedings ArticleDOI
Reinforcement learning based spectrum-aware routing in multi-hop cognitive radio networks
TL;DR: Two adaptive reinforcement learning based spectrum-aware routing protocols are introduced and Q-Learning and Dual Reinforcement Learning are applied respectively, which are simpler and easier to implement, more cost-effective, and can avoid drawbacks in on-demand protocols but still keep adaptive and dynamic routing.
Journal ArticleDOI
The neuronal replicator hypothesis
TL;DR: It is proposed that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes, and evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition.
Journal ArticleDOI
A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment
Christopher M. Glaze,Alexandre L. S. Filipowicz,Joseph W. Kable,Vijay Balasubramanian,Joshua I. Gold +4 more
TL;DR: It is shown that individual variability in learning from noisy evidence involves a bias–variance trade-off that is best explained by a model using a sampling algorithm that approximates optimal inference.
Journal ArticleDOI
Theoretical Frameworks for Neuroeconomics of Intertemporal Choice
TL;DR: In this article, the importance of studying neurochemical and neuroendocrinological modulations of intertemporal choice and time-perception (e.g. serotonin, dopamine, cortisol, testosterone, and epinephrine) is emphasized.
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.
Journal ArticleDOI
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.