Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Shin, Jaeuk"'
Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data usage remains a central challenge to be tackled for its successful real-world applications. In this paper, we propose a sample-efficient meta-RL algorithm that lea
Externí odkaz:
http://arxiv.org/abs/2312.05465
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertainty with limited observations. However, the complexity of POMDPs poses v
Externí odkaz:
http://arxiv.org/abs/2211.14998
Publikováno v:
IEEE Robotics and Automation Letters, 2022
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel algorithm that combines meta-reinforcement learning (meta-RL) with mo
Externí odkaz:
http://arxiv.org/abs/2109.07120
Anderson acceleration for partially observable Markov decision processes: A maximum entropy approach
Publikováno v:
In Automatica May 2024 163
In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of Hamilton-Jacobi-Bellman (HJB) equations derived from applying the dyn
Externí odkaz:
http://arxiv.org/abs/2010.14087
Publikováno v:
In Fuel Processing Technology October 2023 249
Publikováno v:
In Separation and Purification Technology 15 January 2022 281
Publikováno v:
In Journal of Hazardous Materials 5 June 2021 411
Publikováno v:
In Chemical Engineering Journal 1 February 2020 381
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