# Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems

arXiv: https://arxiv.org/abs/2403.17338 · HTML: https://mlsanity.com/paper/2403.17338/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Safe Reinforcement Learning Using Robust MPC](https://mlsanity.com/paper/1906.04005/) (2019)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions](https://mlsanity.com/paper/2105.10596/) (2021)
- [Decentralized Optimal Coordination of Connected and Automated Vehicles for Multiple Traffic Scenarios](https://mlsanity.com/paper/2003.05059/) (2020)
- [Iterative Convex Optimization for Model Predictive Control with Discrete-Time High-Order Control Barrier Functions](https://mlsanity.com/paper/2210.04361/) (2022)
- [Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles](https://mlsanity.com/paper/2305.16818/) (2023)
- [Merging control in mixed traffic with safety guarantees: a safe sequencing policy with optimal motion control](https://mlsanity.com/paper/2305.16725/) (2023)
- [Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging](https://mlsanity.com/paper/2306.01871/) (2023)

## Built on by (most cited first, 7 of 7)

- [Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning](https://mlsanity.com/paper/2512.12046/) (2025)
- [Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems](https://mlsanity.com/paper/2507.14850/) (2025)
- [A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions](https://mlsanity.com/paper/2508.09128/) (2025)
- [Probabilistically safe and efficient model-based reinforcement learning](https://mlsanity.com/paper/2504.00626/) (2025)
- [Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware](https://mlsanity.com/paper/2506.00982/) (2025)
- [A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning](https://mlsanity.com/paper/2604.06463/) (2026)
- [Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields](https://mlsanity.com/paper/2610.20255/) (2026)

## Abstract (arXiv)

Optimal control methods provide solutions to safety-critical problems but easily become intractable. Control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced. Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. %as well as infeasibility. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon Control (RHC) approach leveraging Model Predictive Control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method.