Neural solvers can now handle the combined complexity of coordinating multiple agents with competing objectives, generalizing across different team sizes and problem instances better than conventional heuristics.
CAMO is a neural network solver that helps teams of robots visit multiple locations while balancing competing goals like travel time and total distance. It uses a conditional encoder to handle different preference trade-offs and a collaborative decoder to coordinate multiple robots, outperforming traditional optimization methods on this complex multi-agent, multi-objective problem.