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pro vyhledávání: '"Lim, Han"'
Autor:
Lim, Han-Dong, Lee, Donghwan
Multi-agent reinforcement learning (MARL) has witnessed a remarkable surge in interest, fueled by the empirical success achieved in applications of single-agent reinforcement learning (RL). In this study, we consider a distributed Q-learning scenario
Externí odkaz:
http://arxiv.org/abs/2405.14078
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches. Among these, $Q$-learning has proven to be a powerful algorithm in model-free settings. However, the extension of $Q$-learning t
Externí odkaz:
http://arxiv.org/abs/2402.11877
Autor:
Lim, Han-Dong, Lee, Donghwan
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process. The proposed approach is based on distributed optimization algorithms, which can be interpreted as primal-dual
Externí odkaz:
http://arxiv.org/abs/2310.00638
The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual age
Externí odkaz:
http://arxiv.org/abs/2307.16706
Autor:
Lim, Han-Dong, Lee, Donghwan
Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL). Despite its widespread use, it has only been recently that researchers have begun to actively study its finite time behavior, i
Externí odkaz:
http://arxiv.org/abs/2306.09746
Autor:
Lim, Han-Dong, Lee, Donghwan
Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-difference (TD) learning, is known to suffer form divergence issue when th
Externí odkaz:
http://arxiv.org/abs/2302.09875
Autor:
Lim, Han-Dong, Lee, Donghwan
Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades. Although researches on asymptotic convergence analysis of Q-learning have a long
Externí odkaz:
http://arxiv.org/abs/2207.12217
Autor:
Chae-Eun Oh, Ye-Lim Han, Dong-Ho Lee, Jin-Ha Hwang, Hwan-Seok Jeong, Myeong-Ho Kim, Kyoung-Seok Son, Sunhee Lee, Sang-Hun Song, Hyuck-In Kwon
Publikováno v:
IEEE Journal of the Electron Devices Society, Vol 12, Pp 564-568 (2024)
We demonstrate that the shorter channel self-aligned top-gate (SA TG) coplanar indiumgallium- zinc oxide (IGZO) thin-film transistors (TFTs), with negative voltage applied to the back-gate, exhibit superior characteristics as driving transistors in o
Externí odkaz:
https://doaj.org/article/0ebef8af161c4d67b5095f0a9d0c8274
Autor:
Lim, Han-Dong, Lee, Donghwan
Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develo
Externí odkaz:
http://arxiv.org/abs/2202.05404
Autor:
Kistler, Peter M., Sanders, Prash, Amarena, John V., Bain, Chris R., Chia, Karin M., Choo, Wai-Kah, Eslick, Adam T., Hall, Tanya, Hopper, Ingrid K., Kotschet, Emily, Lim, Han S., Ling, Liang-Han, Mahajan, Rajiv, Marasco, Silvana F., McGuire, Mark A., McLellan, Alex J., Pathak, Rajeev K., Phillips, Karen P., Prabhu, Sandeep, Stiles, Martin K., Sy, Raymond W., Thomas, Stuart P., Toy, Tracey, Watts, Troy W., Weerasooriya, Rukshen, Wilsmore, Bradley R., Wilson, Lauren, Kalman, Jonathan M.
Publikováno v:
In Heart, Lung and Circulation June 2024 33(6):828-881