Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning
arXiv:2604.03785v2 Announce Type: replace Abstract: Communication is essential for coordination in \emph{cooperative} multi-agent reinforcement learning under partial observability, yet \emph{cross-timestep} delays cause messages to arrive multiple timesteps after generation, inducing temporal misalignment and making information stale when consumed. We formalize this setting as a delayed-communication partially observable Markov game (DeComm-POMG) and decompose a message's effect into \emph{communication gain} and \emph{delay cost}, yielding the Communication Gain and Delay Cost (CGDC) metric.
The increasing complexity and distributed nature of AI systems necessitate better methodologies for managing communication delays and ensuring effective coordination.
This research formalizes a critical challenge in multi-agent AI development, offering a framework to design more robust and efficient cooperative AI systems.
Understanding and quantifying communication gain and delay cost allows for optimized communication strategies in cooperative multi-agent reinforcement learning, impacting system design and performance.
- · AI agents developers
- · Robotics
- · Distributed AI systems
- · Inefficient multi-agent training methods
- · Systems with high communication latency
Improved performance and reliability of cooperative multi-agent AI systems in real-world applications requiring complex coordination.
Accelerated deployment of autonomous agent swarms and more sophisticated robotic teams in fields like logistics, defense, and exploration.
Enhanced capabilities for AI systems to operate autonomously and adaptively in dynamic, partially observable environments, blurring human-agent collaboration boundaries.
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Read at arXiv cs.AI