Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

2024-03-11 · Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.

📄 PDF Abstract BibTeX arXiv:2403.06524

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Decision MakingDeep Reinforcement Learningreinforcement-learning Reinforcement Learning

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

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