MARLadona-Towards Cooperative Team Play Using Multi-Agent Reinforcement Learning
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Author / Producer
Date
2025
Publication Type
Conference Paper
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yes
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Abstract
Robot soccer, in its full complexity, poses an unsolved research challenge. Current solutions heavily rely on engineered heuristic strategies, which lack robustness and adaptability. Deep reinforcement learning has gained significant traction in various complex robotics tasks such as locomotion, manipulation, and competitive games (e.g., AlphaZero, OpenAI Five), making it a promising solution to the robot soccer prob lem. This paper introduces MARLadona. A decentralized multi agent reinforcement learning (MARL) training pipeline capable of producing agents with sophisticated team play behavior, bridging the shortcomings of heuristic methods. Furthermore, we created an open-source multi-agent soccer environment. Utilizing our MARL framework and a modified global entity encoder (GEE) as our core architecture, our approach achieves a 66.8 % win rate against HELIOS agent, which employs a state-of-the-art heuristic strategy. In addition, we provided an in-depth analysis of the policy behavior and interpreted the agent’s intention using the critic network.
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published
Editor
Book title
2025 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
15014 - 15020
Publisher
IEEE
Event
42nd IEEE International Conference on Robotics and Automation (ICRA 2025)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems
Notes
Conference lecture held on May 22, 2025
Funding
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Is new version of: https://doi.org/10.48550/arXiv.2409.20326