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cs.LG2025
Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
Yisak Park, Sunwoo Lee, Seungyul Han
Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrate their influence, leading to in…
cs.LG2025
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning
Sanghyeon Lee, Sangjun Bae, Yisak Park +1
Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decompos…
cs.LG2025
Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
Jeongmo Kim, Yisak Park, Minung Kim +1
Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution. While context-based meta-RL methods improve task representati…