Results 11 to 20 of about 199,848 (147)
Journal of the American Statistical Association, 2020 Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss.
Ashkan Ertefaie +3 more
openaire +4 more sources
Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2023 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model.
Uğur Yıldıran
doaj +1 more source
Frontiers in Robotics and AI, 2021 The current pandemic has highlighted the need for rapid construction of structures to treat patients and ensure manufacturing of health care products such as vaccines.
Alex Grimshaw, John Oyekan
doaj +1 more source
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias.
Zhu, Rong, Rigotti, Mattia
openaire +2 more sources
An Improved Q-Learning Algorithm and Its Application in Path Planning
Taiyuan Ligong Daxue xuebao, 2021 Traditional Q-Learning algorithm has the problems of too many random searches and slow convergence speed. Therefore, in this paper an improved ε-Q-Learning algorithm based on traditional Q-Learning algorithm was propased and applied to path planning. The
Guojun MAO, Shimin GU
doaj +1 more source
Machine Learning, 1992 Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
Watkins, C., Dayan, P.
openaire +2 more sources
Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks
IEEE Access, 2022 The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving.
Teodor Ivanescu +3 more
doaj +1 more source
Continuous-Action Q-Learning [PDF]
Machine Learning, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
del R. Millán, José +2 more
openaire +1 more source
Frame Size Optimization for Dynamic Framed Slotted ALOHA in RFID Systems
Jisuanji kexue yu tansuo, 2021 In recent years, the State Grid has actively promoted the construction of ubiquitous power Internet of things, so as to realize the interconnection and optimized management of things in the power system. Specifically, radio frequency identification (RFID)
HE Jindong, BU Yanling, SHI Congcong, XIE Lei
doaj +1 more source
Sparse cooperative Q-learning [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.
Kok, J.R., Vlassis, N.
openaire +3 more sources