9 citations · 20 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
Whiyoung Jung, Myungsik Cho, Jongeui Park +1
Constrained reinforcement learning (RL) is an area of RL whose objective is to find an optimal policy that maximizes expected cumulative return while satisfying a given constraint.…
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
Jeewon Jeon, Woojun Kim, Whiyoung Jung +1
In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward. To tackle this problem, we propose a novel method named MASER: MARL with subgoa…
Robust Imitation Learning against Variations in Environment Dynamics
Jongseong Chae, Seungyul Han, Whiyoung Jung +3
In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed. The existing IL framework trained i…