PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks
Researchers introduce PRIME, a method to address neuron dormancy in multi-agent reinforcement learning under non-stationary conditions. PRIME extends the bidirectional Silent Neuron framework to cooperative settings, reinitializing only neurons that are both activation-dormant and gradient-silent. In a UAV emergency communication simulator, PRIME improves interquartile mean return by 24.9% over MAPPO and reduces dormant neuron fractions to 10–20% compared to 40–45%.
Why it matters: This work offers a principled solution to preserving learning capacity in multi-agent systems facing changing objectives, with implications for robotics and autonomous networks.
Full story at: arXiv Multiagent Systems ↗