Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
<|reference_start|>Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward: 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.<|reference_end|>
@article{pathare2024tactical,
    title={Tactical Decision Making for Autonomous Trucks by Deep Reinforcement
  Learning with Total Cost of Operation Based Reward},
    author={Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani},
    journal={arXiv preprint arXiv:2403.06524},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06524},
    primaryClass={cs.LG cs.AI cs.RO}
}