6 citations · 18 across the 6 of their papers we have counts for
6 papers
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…
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.…
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…
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
Woojun Kim, Whiyoung Jung, Myungsik Cho +1
In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by reg…
Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning
Woojun Kim, Myungsik Cho, Youngchul Sung
In this paper, we propose a new learning technique named message-dropout to improve the performance for multi-agent deep reinforcement learning under two application scenarios: 1)…