Open AccessPosted Content
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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Journal ArticleDOI
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions
TL;DR: The fundamental concepts of supervised, unsupervised, and reinforcement learning are established, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, and the promising approaches for how ML can contribute to supporting each target 5G network requirement are discussed.
Proceedings Article
Layered learning in multiagent systems
TL;DR: Ultimately, this dissertation demonstrates that by learning portions of their cognitive processes, selectively communicating, and coordinating their behaviors via common knowledge, a group of independent agents can work towards a common goal in a complex, real-time, noisy, collaborative, and adversarial environment.
Journal ArticleDOI
Empirical evaluation methods for multiobjective reinforcement learning algorithms
TL;DR: Standard methods for empirical evaluation of multiobjective reinforcement learning algorithms are proposed, and appropriate evaluation metrics and methodologies are proposed for each class.
Proceedings Article
On Kernelized Multi-armed Bandits.
TL;DR: In this article, the authors considered the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown.
Posted Content
Learning to Cooperate via Policy Search
TL;DR: In this article, a gradient-based distributed policy search method for cooperative games is proposed and compared to the notion of local optimum to that of Nash equilibrium, which is a reasonable alternative to value-based methods for partially observable environments.
References
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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
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.
Book
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.
Book
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.
Book
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.