Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications

Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications

Multi-agent reinforcement learning (MARL) algorithms have been widely used for many applications requiring sequential decision-making to maximize the expected rewards through multi-agent cooperation. However, MARL faces significant challenges, particularly in resource-limited real-time computing env...

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Bibliographic Details
Main Authors: Hyunsoo Lee, Gyu Seon Kim, Minseok Choi, Hankyul Baek, Soohyun Park
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Multi-agent reinforcement learning
agent selection
StarCraft multi-agent challenge (SMAC)
Online Access:https://ieeexplore.ieee.org/document/10623680/

Internet

https://ieeexplore.ieee.org/document/10623680/

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