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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 ArticleDOI
Toward End-to-End Control for UAV Autonomous Landing via Deep Reinforcement Learning
Riccardo Polvara,Massimiliano Patacchiola,Sanjay Sharma,Jian Wan,Andrew J. Manning,Robert Sutton,Angelo Cangelosi +6 more
TL;DR: A method based on deep reinforcement learning that only requires low-resolution images coming from a down looking camera in order to drive the vehicle, proving that the underline DQNs are able to generalise effectively on unseen scenarios and proving that in some conditions the network outperformed human pilots.
Journal ArticleDOI
UAV-IoT for Next Generation Virtual Reality
TL;DR: Experimental results demonstrate considerable performance efficiency gains enabled by each system component over the respective state-of-the-art reference methods, in delivered VR immersion fidelity, application interactivity/play-out latency, and transmission power consumption.
Journal ArticleDOI
Controlled gliding and perching through deep-reinforcement-learning
TL;DR: It is shown that reinforcement learning identifies gliding strategies with minimum energy expenditure and fastest time of arrival, with better performance than model-based optimal control, while being robust to perturbations.
Journal ArticleDOI
Accelerating autonomous learning by using heuristic selection of actions
TL;DR: This paper investigates the use of heuristics for increasing the rate of convergence of RL algorithms and contributes with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristic for action selection to the Q-Learning algorithm.
Journal ArticleDOI
Comprehensive comparison of online ADP algorithms for continuous-time optimal control
Yuanheng Zhu,Dongbin Zhao +1 more
TL;DR: This paper reviews the research of online ADP algorithms for the optimal control of continuous-time systems and compares their performance on the same problem.
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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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.