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

arXiv: https://arxiv.org/abs/2403.06524 · HTML: https://mlsanity.com/paper/2403.06524/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control](https://mlsanity.com/paper/1910.12047/) (2019)
- [Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation](https://mlsanity.com/paper/2004.10439/) (2020)
- [Utilizing Reinforcement Learning for de novo Drug Design](https://mlsanity.com/paper/2303.17615/) (2023)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)

## Built on by (most cited first, 3 of 3)

- [Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles](https://mlsanity.com/paper/2605.16858/) (2026)
- [Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles](https://mlsanity.com/paper/2601.18783/) (2026)
- [Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics](https://mlsanity.com/paper/2603.05113/) (2026)

## Abstract (arXiv)

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.