6 citations · 16 across the 10 of their papers we have counts for
4 papers · 1 filter
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.…
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…
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…