Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles
This research developed a multi-objective reinforcement learning framework for heavy-duty trucks to handle tactical decisions like cruising and lane changes while balancing safety, energy efficiency, and time efficiency. The method learns policies that capture trade-offs between these conflicting goals using preference conditions. The resulting Pareto fronts are smooth and interpretable, showing how different operational preferences affect driving behavior.