6 citations · 16 across the 10 of their papers we have counts for
10 papers
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.…
Adaptive -Aid for Conditional Supervised Learning in Offline Reinforcement Learning
Jeonghye Kim, Suyoung Lee, Woojun Kim +1
Offline reinforcement learning (RL) has progressed with return-conditioned supervised learning (RCSL), but its lack of stitching ability remains a limitation. We introduce -Aide…
Domain Adaptive Imitation Learning with Visual Observation
Sungho Choi, Seungyul Han, Woojun Kim +3
In this paper, we consider domain-adaptive imitation learning with visual observation, where an agent in a target domain learns to perform a task by observing expert demonstrations…
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
Woojun Kim, Yongjae Shin, Jongeui Park +1
Deep reinforcement learning (RL) has achieved remarkable success in solving complex tasks through its integration with deep neural networks (DNNs) as function approximators. Howeve…
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
Woojun Kim, Jeonghye Kim, Youngchul Sung
In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of d…
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making
Jeonghye Kim, Suyoung Lee, Woojun Kim +1
The recent success of Transformer in natural language processing has sparked its use in various domains. In offline reinforcement learning (RL), Decision Transformer (DT) is emergi…