1 citations · 1 across the 7 of their papers we have counts for
8 papers
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
Giseung Park, Hyunyoung Nam, Woohyeon Byeon +2
Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While max-min MORL has emerged as…
STAIRS-Former: Spatio-Temporal Attention with Interleaved Recursive Structure Transformer for Offline Multi-task Multi-agent Reinforcement Learning
Jiwon Jeon, Myungsik Cho, Youngchul Sung
Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenari…
Generalized Per-Agent Advantage Estimation for Multi-Agent Policy Optimization
Seongmin Kim, Giseung Park, Woojun Kim +3
In this paper, we propose a novel framework for multi-agent reinforcement learning that enhances sample efficiency and coordination through accurate per-agent advantage estimation.…
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…
Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning
Giseung Park, Youngchul Sung
In this paper, we introduce a simple yet effective reward dimension reduction method to tackle the scalability challenges of multi-objective reinforcement learning algorithms. Whil…
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, Woohyeon Byeon, Seongmin Kim +3
In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min…