Decomposing Communication Gain and Delay Cost Under Cross-Timestep Delays in Cooperative Multi-Agent Reinforcement Learning

1. 1Formalize the problem as a delayed-communication partially observable Markov game (DeComm-POMG)
2. 2Decompose a message's effect into communication gain and delay cost
3. 3Analyze the impact of cross-timestep delays on temporal misalignment and information staleness
4. 4Develop strategies to mitigate the negative effects of delayed communication

AI engineers and researchers working on multi-agent reinforcement learning benefit from this research as it provides a framework for understanding the effects of delayed communication on cooperative tasks

💡 Cross-timestep delays can significantly impact the effectiveness of communication in cooperative multi-agent reinforcement learning

💡 Delayed communication in multi-agent RL can cause temporal misalignment and stale info