GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
📰 ArXiv cs.AI
Learn how GCT-MARL enables sample-efficient cooperative multi-agent reinforcement learning through graph-based contrastive transfer, improving agent adaptability to new environments and tasks
Action Steps
- Build a graph-based representation of the environment using the multi-view graph contrastive backbone of MAIL
- Implement a per-view, adaptively weighted alignment loss to facilitate transfer learning
- Apply a two-phase training protocol to fine-tune the agents for the target task
- Test the GCT-MARL framework in a cooperative multi-agent reinforcement learning setting
- Compare the performance of GCT-MARL with other transfer learning methods
Who Needs to Know This
Researchers and engineers working on multi-agent reinforcement learning can benefit from this framework to improve the efficiency and adaptability of their agents in complex environments
Key Insight
💡 Graph-based contrastive transfer can significantly improve the sample efficiency of cooperative multi-agent reinforcement learning
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🤖 GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning 📈
Key Takeaways
Learn how GCT-MARL enables sample-efficient cooperative multi-agent reinforcement learning through graph-based contrastive transfer, improving agent adaptability to new environments and tasks
Full Article
Title: GCT-MARL: Graph-Based Contrastive Transfer for Sample-Efficient Cooperative Multi-Agent Reinforcement Learning
Abstract:
arXiv:2606.25073v1 Announce Type: cross 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
Abstract:
arXiv:2606.25073v1 Announce Type: cross 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
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