Predicting Multi-Agent Specialization via Task Parallelizability
This work addresses the challenge of optimizing agent strategies in multi-agent systems, providing a diagnostic tool for MARL training, though it is incremental as it builds on distributed systems concepts.
The paper tackled the problem of predicting when specialization improves performance in multi-agent systems by proposing that it depends on task parallelizability, and validated a closed-form bound on MARL benchmarks with close alignment to empirical specialization measures.
When should we encourage specialization in multi-agent systems versus train generalists that perform the entire task independently? We propose that specialization largely depends on task parallelizability: the potential for multiple agents to execute task components concurrently. Drawing inspiration from Amdahl's Law in distributed systems, we present a closed-form bound that predicts when specialization improves performance, depending only on task concurrency and team size. We validate our model on two standard MARL benchmarks that represent opposite regimes -- StarCraft Multi-Agent Challenge (SMAC, unlimited concurrency) and Multi-Particle Environment (MPE, unit-capacity bottlenecks) -- and observe close alignment between the bound at each extreme and an empirical measure of specialization. Three follow-up experiments in Overcooked-AI demonstrate that the model works in environments with more complex spatial and resource bottlenecks that allow for a range of strategies. Beyond prediction, the bound also serves as a diagnostic tool, highlighting biases in MARL training algorithms that cause sub-optimal convergence to specialist strategies with larger state spaces.