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cs.LG2026
Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
Byeonghu Na, Hyungho Na, Yeongmin Kim +4
Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimiz…
cs.LG2025
Trajectory-Class-Aware Multi-Agent Reinforcement Learning
Hyungho Na, Kwanghyeon Lee, Sumin Lee +1
In the context of multi-agent reinforcement learning, generalization is a challenge to solve various tasks that may require different joint policies or coordination without relying…
cs.LG2024★ 3 cited
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
Hyungho Na, Yunkyeong Seo, Il-chul Moon
In cooperative multi-agent reinforcement learning (MARL), agents aim to achieve a common goal, such as defeating enemies or scoring a goal. Existing MARL algorithms are effective b…