How can I make my LLM agents' communication more robust?
Robust Multi-agent Communication Based on Decentralization-Oriented Adversarial Training
This paper addresses the robustness of communication in multi-agent reinforcement learning (MARL) systems. Existing methods often create unbalanced communication structures where a few channels are heavily used, making the system vulnerable if those channels fail. The proposed solution, DMAC, uses adversarial training to encourage a more decentralized communication pattern, improving robustness against attacks and overall system performance.
DMAC's relevance to LLM-based multi-agent systems stems from its focus on robust communication, which is crucial for effective collaboration between LLM agents. By decentralizing communication, DMAC can improve the resilience of LLM-based multi-agent applications against individual agent failures or targeted attacks on communication channels. The dynamic identification and masking of critical communication channels by DMAC_Adv offers a novel approach to identifying vulnerabilities and improving the reliability of interactions within these systems.