331 citations · 670 across the 9 of their papers we have counts for
8 papers
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Shiqi Lei, Kanghoon Lee, Linjing Li +2
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environm…
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Huijie Tang, Federico Berto, Zihan Ma +3
Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultane…
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Chuanbo Hua, Federico Berto, Michael Poli +2
While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only re…
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Kanghoon Lee, Jiachen Li, David Isele +3
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior p…
Computing Algorithm for an Equilibrium of the Generalized Stackelberg Game
Jaeyeon Jo, Jihwan Yu, Jinkyoo Park
The generalized Stackelberg game (single-leader multi-follower game) is intricately intertwined with the interaction between a leader and followers (hierarchical interaction)…
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Jiwoo Son, Minsu Kim, Hyeonah Kim +1
This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre…