MARLadona - Towards Cooperative Team Play Using Multi-Agent Reinforcement Learning

2024-09-30

arXiv.org

AI Summary

To address the poor robustness and limited adaptability of handcrafted heuristic strategies in robot soccer, this paper proposes a decentralized multi-agent reinforcement learning (MARL) framework for high-coordination team decision-making. Methodologically, it introduces two key innovations: (1) a novel MARL architecture integrating an enhanced Global Entity Encoder (GEE) to improve state representation and model individual–global relational dynamics; and (2) a critic-network-driven intention decoding mechanism, enabling, for the first time in an open-source multi-agent soccer environment (developed in-house), interpretable policy intention identification and behavioral attribution analysis. Experimental results demonstrate a 66.8% win rate against the top-performing heuristic team HELIOS, significantly improving coordination stability and policy transparency. The framework establishes a scalable and interpretable paradigm for embodied multi-agent collaboration.

Technology Category

• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 problem. 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.

Problem

• Addresses robot soccer complexity using MARL.
• Replaces heuristic strategies with adaptive learning.
• Achieves high win rate against top heuristics.

Innovation

• Decentralized multi-agent reinforcement learning pipeline
• Open-source multi-agent soccer environment
• Modified global entity encoder architecture