#learned-dynamics News & Analysis

Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering

Researchers propose a modular state-estimation layer that enhances pre-trained multi-agent reinforcement learning (MARL) policies by compensating for communication delays and packet loss through learned dynamics filtering. The plug-and-play approach combines gated transition models with Kalman filtering to estimate current states from delayed observations, demonstrating significant robustness improvements without requiring retraining of original policies.