Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns

TL;DR

This paper introduces a safe reinforcement learning framework that embeds safety directly into the action space using forward-invariance, enabling safe policy learning without runtime constraints.

Contribution

It presents a novel action-space design based on forward-invariance, allowing RL to learn policies that are inherently safe by construction.

Findings

Validated on a quadcopter hover task under disturbance

Learned policies improve performance and switching efficiency

All policies maintain safety-preserving properties

Abstract

This paper proposes a safe reinforcement learning (RL) framework based on forward-invariance-induced action-space design. The control problem is cast as a Markov decision process, but instead of relying on runtime shielding or penalty-based constraints, safety is embedded directly into the action representation. Specifically, we construct a finite admissible action set in which each discrete action corresponds to a stabilizing feedback law that preserves forward invariance of a prescribed safe state set. Consequently, the RL agent optimizes policies over a safe-by-construction policy class. We validate the framework on a quadcopter hover-regulation problem under disturbance. Simulation results show that the learned policy improves closed-loop performance and switching efficiency, while all evaluated policies remain safety-preserving. The proposed formulation decouples safety assurance from performance optimization and provides a promising foundation for safe learning in nonlinear systems.