A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
This work addresses safety concerns in reinforcement learning for applications like robotics or autonomous systems, but it appears incremental as it builds on existing MPC and CBF methods.
The paper tackles the challenge of ensuring safety in reinforcement learning under unknown and stochastic dynamics by proposing a model predictive control-based framework called PECTS, which learns system dynamics and control barrier functions to enforce probabilistic safety constraints, and it outperforms baseline methods in simulations.
Ensuring safety under unknown and stochastic dynamics remains a significant challenge in reinforcement learning (RL). In this paper, we propose a model predictive control (MPC)-based safe RL framework, called Probabilistic Ensembles with CBF-constrained Trajectory Sampling (PECTS), to address this challenge. PECTS jointly learns stochastic system dynamics with probabilistic neural networks (PNNs) and control barrier functions (CBFs) with Lipschitz-bounded neural networks. Safety is enforced by incorporating learned CBF constraints into the MPC formulation while accounting for the model stochasticity. This enables probabilistic safety under model uncertainty. To solve the resulting MPC problem, we utilize a sampling-based optimizer together with a safe trajectory sampling method that discards unsafe trajectories based on the learned system model and CBF. We validate PECTS in various simulation studies, where it outperforms baseline methods.