GNES: Neural-Guided Evolutionary Program Search for Interpretable Multi-Agent Control

Synthesizing interpretable controllers for multi-agent systems requires optimizing discrete symbolic programs from sparse, expensive, long-horizon simulator feedback. Evolutionary program search preserves interpretability but often spends many evaluations on weak random variation, whereas deep multi-agent reinforcement learning can learn effective policies that are difficult to inspect. We propose GNES, a neural-guided evolutionary program search framework for symbolic controller synthesis. GNES casts controller design as a program-edit decision process: gene growth trees are program states, validity-preserving genetic operators are actions, simulator fitness provides the return, and policy--value graph neural networks learn edit signals over controller topology. Monte Carlo tree search performs look-ahead planning in controller-program space before simulator validation, closing the loop between evolution, learned guidance, and verified rollouts. Experiments on three decentralized AirSim swarm-control benchmarks show that GNES improves search efficiency and final controller quality relative to evolutionary and reinforcement-learning baselines, while automatically evolving compact, inspectable distributed velocity controllers. The discovered policies expose coordination motifs, such as density-normalized radial gains and motion-compensating terms, that differ from standard hand-designed swarm rules. Ablations and learned-search comparisons indicate that graph-structured policy--value learning and MCTS look-ahead both contribute to the gains.