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

TL;DR

This paper introduces a multi-objective reinforcement learning framework for trucks that learns a continuous set of Pareto-optimal policies balancing safety, energy, and time efficiency, enabling flexible and adaptive decision-making.

Contribution

It presents a novel PPO-based multi-objective RL approach that explicitly models trade-offs and produces a smooth, interpretable Pareto frontier for tactical highway truck decisions.

Findings

Learns a continuous set of Pareto-optimal policies.

Produces a smooth, interpretable Pareto frontier.

Enables seamless policy transitions without retraining.

Abstract

Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles. A central difficulty is that conventional scalar reward formulations, obtained by aggregating these competing objectives, often obscure the structure of their trade-offs. We present a Proximal Policy Optimization based multi-objective reinforcement learning framework that learns a continuous set of policies explicitly representing these trade-offs and evaluates it on a scalable simulation platform for tactical decision making in trucks. The proposed approach learns a continuous set of Pareto-optimal policies that capture the trade-offs among three conflicting objectives: safety, quantified in terms of collisions and successful completion; energy efficiency and time efficiency, quantified using energy cost and driver cost, respectively. The resulting Pareto frontier is smooth and interpretable, enabling flexibility in choosing driving behavior along different conflicting objectives. This framework allows seamless transitions between different driving policies without retraining, yielding a robust and adaptive decision-making strategy for autonomous trucking applications.