most citedRobust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms

7 citations · 7 across the 2 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2023

SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process

Zichong Li, Yanbo Xu, Simiao Zuo +4

Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of e…

cs.LG2023★ 7 cited

Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms

Alexander Bukharin, Yan Li, Yue Yu +6

Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…