A Provable Approach for End-to-End Safe Reinforcement Learning

TL;DR

This paper introduces PLS, a provably safe reinforcement learning method that guarantees policy safety during learning and deployment, combining offline supervised learning with cautious online optimization.

Contribution

It presents a novel approach integrating offline safe RL with safe deployment, providing theoretical safety guarantees and improved empirical performance.

Findings

PLS guarantees safety with high probability.

PLS achieves higher rewards compared to baselines.

PLS outperforms existing methods in safety and reward metrics.

Abstract

A longstanding goal in safe reinforcement learning (RL) is a method to ensure the safety of a policy throughout the entire process, from learning to operation. However, existing safe RL paradigms inherently struggle to achieve this objective. We propose a method, called Provably Lifetime Safe RL (PLS), that integrates offline safe RL with safe policy deployment to address this challenge. Our proposed method learns a policy offline using return-conditioned supervised learning and then deploys the resulting policy while cautiously optimizing a limited set of parameters, known as target returns, using Gaussian processes (GPs). Theoretically, we justify the use of GPs by analyzing the mathematical relationship between target and actual returns. We then prove that PLS finds near-optimal target returns while guaranteeing safety with high probability. Empirically, we demonstrate that PLS outperforms baselines both in safety and reward performance, thereby achieving the longstanding goal to obtain high rewards while ensuring the safety of a policy throughout the lifetime from learning to operation.