Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints
📰 ArXiv cs.AI
Learn to decouple communication from policy in multi-agent reinforcement learning (MARL) to improve robustness under bandwidth constraints
Action Steps
- Identify the coupled bottleneck in existing MARL architectures
- Decouple communication from policy using separate latent representations
- Implement a bandwidth-constrained communication protocol
- Test the robustness of the decoupled MARL system under various bandwidth conditions
- Apply the decoupled approach to real-world MARL applications
Who Needs to Know This
Researchers and engineers working on MARL applications, such as drone swarms or autonomous vehicles, can benefit from this approach to improve coordination and robustness under limited bandwidth conditions
Key Insight
💡 Decoupling communication from policy in MARL can improve robustness and coordination under severe bandwidth constraints
Share This
🤖 Improve MARL robustness under bandwidth constraints by decoupling communication from policy! 📱💻
Key Takeaways
Learn to decouple communication from policy in multi-agent reinforcement learning (MARL) to improve robustness under bandwidth constraints
Full Article
Title: Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints
Abstract:
arXiv:2605.21085v1 Announce Type: cross Abstract: Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe bandwidth constraints. Many communication architectures still expose a coupled bottleneck in which a shared latent representation is used for both policy execution and inter-agent communication. Consequently, reducing message size directly limits the policy's latent spac
Abstract:
arXiv:2605.21085v1 Announce Type: cross Abstract: Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe bandwidth constraints. Many communication architectures still expose a coupled bottleneck in which a shared latent representation is used for both policy execution and inter-agent communication. Consequently, reducing message size directly limits the policy's latent spac
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