GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
arXiv:2606.25073v1 Announce Type: new Abstract: In cooperative multi-agent reinforcement learning (MARL), from a deployment perspective, it is challenging and expensive to train agents from scratch for each new environment or task. In this work, we propose GCT-MARL, a transfer learning framework that builds on the multi-view graph contrastive backbone of MAIL and augments it with a per-view, adaptively weighted alignment loss and a two-phase training protocol specifically designed for transfer across populations of varying sizes and compositions. We empirically demonstrate that the proposed fr
The increasing complexity and scale of multi-agent systems necessitate more efficient training paradigms, leading to a focus on transfer learning solutions to mitigate computational costs and deployment challenges.
This development allows for faster, more scalable deployment of cooperative multi-agent AI systems across diverse and dynamic environments, significantly reducing development overhead.
The ability to efficiently transfer learned behaviors between multi-agent systems of varying sizes and compositions makes advanced AI deployments more practical and financially viable.
- · AI developers
- · Robotics companies
- · Logistics and autonomous systems sectors
- · Enterprises deploying multi-agent AI
- · Companies relying on traditional, 'train from scratch' MARL methods
- · Labor-intensive AI deployment services
Reduced compute costs and faster development cycles for complex multi-agent AI applications.
Accelerated adoption of advanced autonomous systems in various industries, leading to increased automation.
Enhanced overall AI capabilities could drive further research into complex emergent behaviors and their societal implications.
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Read at arXiv cs.LG