Coordination-driven learning in multi-agent problem spaces
This addresses coordination challenges in multi-agent systems, particularly for domains like adversary-aware RL, but appears incremental as it builds on existing MARL frameworks.
The paper tackles the problem of improving coordination in multi-agent reinforcement learning by introducing a novel method to quantify and optimize it as a direct learning objective, with potential applications in adversary-aware RL.
We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains. To this end, we present a novel means of quantifying coordination in multi-agent systems, and discuss the implications of using such a measure to optimize coordinated agent policies. This concept has important implications for adversary-aware RL, which we take to be a sub-domain of multi-agent learning.