3 papers
cs.LG2026
Multi: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
Sangeun Park, Minhae Kwon
A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments. While recen…
cs.LG2025
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
Dongsu Lee, Minhae Kwon
The goal of offline reinforcement learning (RL) is to extract a high-performance policy from the fixed datasets, minimizing performance degradation due to out-of-distribution (OOD)…
cs.LG2024
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
Dongsu Lee, Minhae Kwon
Understanding cognitive processes in multi-agent interactions is a primary goal in cognitive science. It can guide the direction of artificial intelligence (AI) research toward soc…