220 citations · 298 across the 4 of their papers we have counts for
6 papers
Curiosity-Driven Multi-Agent Exploration with Mixed Objectives
Roben Delos Reyes, Kyunghwan Son, Jinhwan Jung +2
Intrinsic rewards have been increasingly used to mitigate the sparse reward problem in single-agent reinforcement learning. These intrinsic rewards encourage the agent to look for…
Information Source Finding in Networks: Querying with Budgets
Jaeyoung Choi, Sangwoo Moon, Jiin Woo +3
In this paper, we study a problem of detecting the source of diffused information by querying individuals, given a sample snapshot of the information diffusion graph, where two que…
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Sungsoo Ahn, Roben Delos Reyes +2
QTRAN is a multi-agent reinforcement learning (MARL) algorithm capable of learning the largest class of joint-action value functions up to date. However, despite its strong theoret…
Solving Continual Combinatorial Selection via Deep Reinforcement Learning
Hyungseok Song, Hyeryung Jang, Hai H. Tran +5
We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intr…
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang +2
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently. Ho…
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Daewoo Kim, Sangwoo Moon, David Hostallero +4
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are…