12 citations · 22 across the 6 of their papers we have counts for
9 papers
State Advantage Weighting for Offline RL
Jiafei Lyu, Aicheng Gong, Le Wan +2
We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage that we commonly adopt in QSA learning, we leverage state adv…
Decentralized Policy Optimization
Kefan Su, Zongqing Lu
The study of decentralized learning or independent learning in cooperative multi-agent reinforcement learning has a history of decades. Recently empirical studies show that indepen…
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Ziluo Ding, Wanpeng Zhang, Junpeng Yue +3
We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also…
Multi-Agent Automated Machine Learning
Zhaozhi Wang, Kefan Su, Jian Zhang +4
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA…
Revisiting Prioritized Experience Replay: A Value Perspective
Ang A. Li, Zongqing Lu, Chenglin Miao
Experience replay enables off-policy reinforcement learning (RL) agents to utilize past experiences to maximize the cumulative reward. Prioritized experience replay that weighs exp…
Learning Fairness in Multi-Agent Systems
Jiechuan Jiang, Zongqing Lu
Fairness is essential for human society, contributing to stability and productivity. Similarly, fairness is also the key for many multi-agent systems. Taking fairness into multi-ag…