# Optimal control barrier functions for RL based safe powertrain control

arXiv: https://arxiv.org/abs/2405.11391 · HTML: https://mlsanity.com/paper/2405.11391/

## 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 7 of its 17 references that are in the map.
- First posted on arXiv 18 May 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, 7 of 7)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach](https://mlsanity.com/paper/1506.02188/) (2015)
- [Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics](https://mlsanity.com/paper/2001.00449/) (2020)
- [Comparison between safety methods control barrier function vs. reachability analysis](https://mlsanity.com/paper/2106.13176/) (2021)
- [Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.07594/) (2022)
- [Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control](https://mlsanity.com/paper/2301.00884/) (2023)

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

- [Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control](https://mlsanity.com/paper/2502.03640/) (2025)
- [Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control](https://mlsanity.com/paper/2606.24010/) (2026)
- [Toward Integrating Adaptive Experience Replay and Online Uncertainty Estimation in Safe Actor-Critic Optimal Control](https://mlsanity.com/paper/2608.04732/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) can improve control performance by seeking to learn optimal control policies in the end-use environment for vehicles and other systems. To accomplish this, RL algorithms need to sufficiently explore the state and action spaces. This presents inherent safety risks, and applying RL on safety-critical systems like vehicle powertrain control requires safety enforcement approaches. In this paper, we seek control-barrier function (CBF)-based safety certificates that demarcate safe regions where the RL agent could optimize the control performance. In particular, we derive optimal high-order CBFs that avoid conservatism while ensuring safety for a vehicle in traffic. We demonstrate the workings of the high-order CBF with an RL agent which uses a deep actor-critic architecture to learn to optimize fuel economy and other driver accommodation metrics. We find that the optimized high-order CBF allows the RL-based powertrain control agent to achieve higher total rewards without any crashes in training and evaluation while achieving better accommodation of driver demands compared to previously proposed exponential barrier function filters and model-based baseline controllers.