no code implementations • 23 May 2024 • Huajie Qian, Donghao Ying, Henry Lam, Wotao Yin
Bagging is a popular ensemble technique to improve the accuracy of machine learning models.
no code implementations • 15 Feb 2023 • Donghao Ying, Yuhao Ding, Alec Koppel, Javad Lavaei
The objective is to find a localized policy that maximizes the average of the team's local utility functions without the full observability of each agent in the team.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 22 May 2022 • Donghao Ying, Mengzi Amy Guo, Hyunin Lee, Yuhao Ding, Javad Lavaei, Zuo-Jun Max Shen
In the exact setting, we prove an $O(T^{-1/3})$ convergence rate for both the average optimality gap and constraint violation, which further improves to $O(T^{-1/2})$ under strong concavity of the objective in the occupancy measure.
no code implementations • 17 Oct 2021 • Donghao Ying, Yuhao Ding, Javad Lavaei
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value function while satisfying constraints on the expected total utility.