Results 31 to 40 of about 544,487 (316)
Reinforcement Learning: Theory and Applications in HEMS
Energies, 2022 The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article.
Omar Al-Ani, Sanjoy Das
doaj +1 more source
Journal of the American Statistical Association, 2015 In this article, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman 2001) under high-dimensional settings. The innovations are three-fold.
Ruoqing Zhu +2 more
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Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey [PDF]
arXiv, 2021 Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision.
arxiv
Quantum Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 13 pages, 7 figures ...
Han-Xiong Li +3 more
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Momentum in Reinforcement Learning [PDF]
, 2019 We adapt the optimization's concept of momentum to reinforcement learning. Seeing the state-action value functions as an analog to the gradients in optimization, we interpret momentum as an average of consecutive $q$-functions. We derive Momentum Value Iteration (MoVI), a variation of Value Iteration that incorporates this momentum idea.
Vieillard, Nino +3 more
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Causal Reinforcement Learning: A Survey [PDF]
arXiv, 2023 Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world ...
arxiv
Learning to reinforcement learn
, 2016 17 pages, 7 figures, 1 ...
Wang, Jane +8 more
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Bayesian Reinforcement Learning [PDF]
, 2012 This chapter surveys recent lines of work that use Bayesian techniques for reinforcement learning. In Bayesian learning, uncertainty is expressed by a prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data observed.
Vlassis, Nikos +3 more
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Inverse Reinforcement Learning without Reinforcement Learning
, 2023 Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) problem as a subroutine. This is counter-
Swamy, Gokul +3 more
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Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
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