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arXiv AI By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles

Read the original on arXiv AI →

The paper introduces a preference-conditioned multi-objective reinforcement learning framework for tactical decision making in heavy-duty vehicles. It explicitly models safety, energy efficiency, and time efficiency as separate objectives and uses Generalized Policy Improvement and Proximal Policy Optimization to learn policies that approximate the Pareto front across these conflicting goals. Experiments in a scalable highway traffic simulation show smooth, interpretable Pareto fronts and meaningful trade-offs, with the policy enabling behavior transitions without retraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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