A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning

TL;DR

This paper introduces PECTS, a safe reinforcement learning framework combining probabilistic neural networks, control barrier functions, and model predictive control to ensure safety under uncertain dynamics.

Contribution

It develops a novel MPC-based safe RL approach that integrates learned CBF constraints with probabilistic models for enhanced safety guarantees.

Findings

PECTS outperforms baseline methods in simulation studies.

It effectively enforces safety under stochastic dynamics.

The framework successfully incorporates model uncertainty into safety constraints.

Abstract

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.