ReinVBC: A Model-based Reinforcement Learning Approach to Vehicle Braking Controller

We present ReBound (reset-aware semi-Markov bootstrapping), a value-learning method for reset-free reinforcement learning (RL) that learns agile driving through real-world RL without human intervention after training starts. Reset-free RL explores by alternating between a forward policy that performs the task and a reset policy that restores a drivable state. Standard reset-free RL treats collisions as episode terminations in value-function bootstrapping, truncating the return at each collision. This conflicts with the reset-free objective, under which driving resumes after recovery, and acts as a hidden collision penalty absent from the reward function. ReBound instead applies semi-Markov bootstrapping: it treats the collision-causing action and the subsequent recovery as a single semi-Markov transition and bootstraps from the post-recovery value, discounted by the actual recovery time. We further apply reward centering to stabilize value learning in the resulting continuing task. In simulation and on a 1/10-scale vehicle, ReBound substantially outperforms conventional value-learning methods and model-based control.

Model Predictive Control (MPC) provides a structured and constraint-aware mechanism for decision-making, but its reliance on optimization-friendly analytical dynamics models limits its use in tasks with contacts and other hard-to-model state dependencies. Model-free reinforcement learning avoids explicit modeling assumptions but typically requires large amounts of interaction data. We present a learning-based MPC framework that combines the data efficiency and structure of local model-based planning with learned components that compensate for incomplete dynamics and finite-horizon myopia. The method augments a nominal analytical model with a residual dynamics network that learns missing state-dependent effects from data and combines the resulting planner with a learned action-value critic that injects long-horizon MDP structure into the local iLQR optimization. To make this practical at reinforcement-learning scale, we develop a GPU-accelerated batched iLQR solver that evaluates learned dynamics and critic networks inside the optimal-control loop and solves thousands of trajectory-optimization problems in parallel. The complete system is integrated into a robotics simulator, enabling scalable model-based reinforcement learning under incomplete dynamics. Experiments on biased and incompletely modeled control tasks show that the approach improves closed-loop control performance while preserving the model-based structure needed for efficient constrained trajectory optimization.

Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand-crafted using heuristics that can be difficult to tune. We propose a Control Barrier Function (CBF)-informed reward design for Multi-Agent RL (MARL) that converts CBF constraint values under joint MARL actions into a reward signal that explicitly guides safe learning. We compare against two heuristic reward baselines in a four-way multi-lane intersection with connected and automated vehicles. Results show that our method achieves the highest task performance and is less sensitive to reward hyperparameters, yielding consistently strong performance across the tested hyperparameter range. Code for reproducing the experimental results and a video demonstration are available at https://github.com/bassamlab/SigmaRL.