arXiv Machine Learning August 19, 2026 By Torben Schiz, Pedro H. J. Nardelli, Henrik Ebel

Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs

Read the original on arXiv Machine Learning →

The paper proposes using encoder‑decoder networks based on LSTM cells to reduce communication in distributed model predictive control (DMPC). Agents transmit a compressed representation of their messages, which receivers reconstruct, thereby lowering the data exchanged each time step. Experiments with mobile robot formations show that the trained networks maintain performance even when full communication is unavailable, and they can adapt to different prediction horizons without retraining.

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