no code implementations • 16 Apr 2024 • Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu
This is the first theoretical result for randomized exploration in cooperative MARL.
no code implementations • 11 Mar 2024 • Hao-Lun Hsu, Qitong Gao, Miroslav Pajic
Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i. e., continuous DBS (cDBS).
1 code implementation • 24 Dec 2023 • Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
Specifically, we assume there is a local reward for each hyperedge, and the reward of the joint arm is the sum of these local rewards.
no code implementations • 12 Jun 2023 • Juncheng Dong, Hao-Lun Hsu, Qitong Gao, Vahid Tarokh, Miroslav Pajic
In this work, we extend the two-player game by introducing an adversarial herd, which involves a group of adversaries, in order to address ($\textit{i}$) the difficulty of the inner optimization problem, and ($\textit{ii}$) the potential over pessimism caused by the selection of a candidate adversary set that may include unlikely scenarios.
no code implementations • 29 Sep 2021 • Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.