4 papers · 1 filter
Rapid Learning in Constrained Minimax Games with Negative Momentum
Zijian Fang, Zongkai Liu, Chao Yu +1
In this paper, we delve into the utilization of the negative momentum technique in constrained minimax games. From an intuitive mechanical standpoint, we introduce a novel framewor…
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
Zongkai Liu, Qian Lin, Chao Yu +4
Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, m…
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
Qian Lin, Zongkai Liu, Danying Mo +1
In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences…
Policy-regularized Offline Multi-objective Reinforcement Learning
Qian Lin, Chao Yu, Zongkai Liu +1
In this paper, we aim to utilize only offline trajectory data to train a policy for multi-objective RL. We extend the offline policy-regularized method, a widely-adopted approach f…