4 papers
Adaptive Action Chunking via Multi-Chunk Q Value Estimation
Yongjae Shin, Jongseong Chae, Seongmin Kim +2
Action chunking emerged as a pivotal technique in imitation learning, enabling policies to predict cohesive action sequences rather than single actions. Recently, this approach has…
Flow Matching with Injected Noise for Offline-to-Online Reinforcement Learning
Yongjae Shin, Jongseong Chae, Jongeui Park +1
Generative models have recently demonstrated remarkable success across diverse domains, motivating their adoption as expressive policies in reinforcement learning (RL). While they…
Flow Actor-Critic for Offline Reinforcement Learning
Jongseong Chae, Jongeui Park, Yongjae Shin +3
The dataset distributions in offline reinforcement learning (RL) often exhibit complex and multi-modal distributions, necessitating expressive policies to capture such distribution…
Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
Woohyeon Byeon, Giseung Park, Jongseong Chae +2
In this paper, we propose a provably convergent and practical framework for multi-objective reinforcement learning with max-min criterion. From a game-theoretic perspective, we ref…