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Multi-Agent Reinforcement Learning with Communication-Constrained Priors
arXiv – CS AI|Guang Yang, Tianpei Yang, Jingwen Qiao, Yanqing Wu, Jing Huo, Xingguo Chen, Yang Gao||3 views
🤖AI Summary
Researchers propose a new multi-agent reinforcement learning framework that addresses communication constraints in real-world scenarios. The approach uses communication-constrained priors to distinguish between lossy and lossless messages, improving learning effectiveness in complex environments with unreliable communication.
Key Takeaways
- →A generalized communication-constrained model is introduced to characterize different communication conditions across scenarios.
- →The framework decouples the impact of lossy versus lossless messages on distributed decision-making using dual mutual information estimation.
- →Communication message impact is quantified into global reward systems for better multi-agent coordination.
- →The approach addresses scalability and robustness limitations in existing multi-agent reinforcement learning systems.
- →Validation shows effectiveness across multiple communication-constrained benchmarks.
#multi-agent-reinforcement-learning#communication-constraints#machine-learning#distributed-systems#ai-research#cooperative-learning#arxiv
Read Original →via arXiv – CS AI
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