9 citations · 20 across the 10 of their papers we have counts for
10 papers
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…
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning
Woojun Kim, Youngchul Sung
Handling the problem of scalability is one of the essential issues for multi-agent reinforcement learning (MARL) algorithms to be applied to real-world problems typically involving…
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning
Woojun Kim, Whiyoung Jung, Myungsik Cho +1
In this paper, we propose a new mutual information framework for multi-agent reinforcement learning to enable multiple agents to learn coordinated behaviors by regularizing the acc…
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…