11 papers
Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
Yonghyeon Jo, Sunwoo Lee, Seungyul Han
Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to ad…
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning
Jaebak Hwang, Sanghyeon Lee, Jeongmo Kim +1
Long-horizon goal-conditioned tasks pose fundamental challenges for reinforcement learning (RL), particularly when goals are distant and rewards are sparse. While hierarchical and…
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…
Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning
Minung Kim, Jeongmo Kim, Gwanwoo Choi +1
Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ.…
Shaping Zero-Shot Coordination via State Blocking
Mingu Kang, Sunwoo Lee, Yonghyeon Jo +1
Zero-shot coordination (ZSC) aims to enable agents to cooperate with independently trained partners without prior interaction, a key requirement for real-world multi-agent systems…
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…