The paper proposes a hybrid control framework that combines finite‑horizon Model Predictive Control (MPC) with Reinforcement Learning (RL) for autonomous highway driving of heavy‑duty trucks. The MPC uses a pre‑trained RL model to estimate terminal costs, enabling effective short‑horizon planning while respecting safety constraints. In simulation with reactive traffic, the hybrid approach outperforms pure MPC, pure RL, and other baseline methods, achieving zero collisions and the lowest overall cost.
By Hooman Hamze, Benjamin Houshmand, Deepthi Pathare, Erik B\"orve, Morteza Haghir Chehreghani, Nikolce Murgovski
arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.
By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
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.
By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
The paper introduces Composite‑Gradient Learning (CGL), a method that explicitly incorporates a model predictive controller (MPC) into the training of a deep reinforcement learning (DRL) agent by treating their control inputs as a joint action. CGL updates the DRL policy while accounting for the interaction with the MPC, unlike prior approaches that view MPC merely as part of the environment. Experiments on two freeway traffic networks show that CGL performs better than alternative methods when the interaction between DRL and MPC is strong, though overall gains are modest.
By Giray \"On\"ur, Azita Dabiri, Bart De Schutter
arXiv:2610.19429v1 Announce Type: new
Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, i...
By Dechen Xie, Mushuang Liu
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.
By Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae