Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic

Researchers present a multi-objective reinforcement learning framework using Proximal Policy Optimization to optimize tactical decision-making for autonomous trucks on highways. The system learns Pareto-optimal policies that balance competing objectives—safety, energy efficiency, and time efficiency—without requiring retraining when switching between different driving behaviors.

This research addresses a fundamental challenge in autonomous vehicle development: reconciling multiple conflicting objectives without reducing them to a single scalar reward that obscures critical trade-offs. Traditional reinforcement learning approaches aggregate safety, efficiency, and cost metrics into one reward signal, potentially hiding the structural relationships between these competing goals. By adopting multi-objective RL, the researchers enable the system to explicitly map out the Pareto frontier—the set of optimal solutions where improving one objective necessarily degrades another.

The work builds on established RL techniques but applies them to a practical domain where real-world consequences matter significantly. Heavy-duty trucking represents a substantial economic sector where autonomous decision-making could deliver measurable value through reduced fuel consumption and improved route optimization, while maintaining strict safety standards. The ability to smoothly transition between policies along the Pareto frontier offers operational flexibility that single-policy systems cannot provide.

For the autonomous vehicle industry, this framework demonstrates technical progress toward deployable solutions that can balance competing regulatory requirements, economic pressures, and safety mandates. The interpretable nature of the Pareto frontier enables stakeholders to understand and audit decision-making trade-offs, addressing transparency concerns that regulators increasingly demand. Rather than having engineers manually tune a scalar reward function through trial-and-error, this approach automates the exploration of the decision space systematically.

Future development should focus on validating these policies in real-world conditions and determining how environmental variability affects the Pareto frontier. Integration with fleet management systems and regulatory compliance frameworks will be essential for commercial deployment.

• →Multi-objective RL enables autonomous trucks to optimize safety, energy, and time efficiency simultaneously while maintaining interpretability.
• →Pareto-optimal policies eliminate manual reward tuning by automatically mapping trade-offs between competing objectives.
• →The framework allows seamless policy switching without retraining, providing operational flexibility for diverse driving scenarios.
• →Smooth Pareto frontiers enable regulatory alignment by making decision-making trade-offs transparent and auditable.
• →Scalable simulation validation demonstrates feasibility for real-world autonomous trucking applications.