5 papers · 1 filter
Simple Actors and Deep Critics for Scalable Reinforcement Learning
Guhyeon Kang, Jaehwi Lee, Minhae Kwon
Recent progress in offline reinforcement learning (RL) has been driven by expressive generative actors such as diffusion and flow-matching policies, which capture multimodal behavi…
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…
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)…
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…
AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Dataset
Dongsu Lee, Chanin Eom, Minhae Kwon
Offline reinforcement learning has emerged as a promising technology by enhancing its practicality through the use of pre-collected large datasets. Despite its practical benefits,…