Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning

TL;DR

This paper introduces a novel offline RL method that learns safety-constrained policies using latent safety modeling and reward optimization, ensuring safety and high performance in complex scenarios.

Contribution

It proposes a latent safety-constrained policy framework using Conditional Variational Autoencoders and reward-Advantage Weighted Regression, with theoretical guarantees and superior empirical results.

Findings

Outperforms existing methods in safety and reward metrics

Maintains safety compliance in autonomous driving scenarios

Provides theoretical bounds on policy performance

Abstract

In safe offline reinforcement learning (RL), the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only offline data. Traditional methods often face difficulties in balancing these constraints, leading to either diminished performance or increased safety risks. We address these issues with a novel approach that begins by learning a conservatively safe policy through the use of Conditional Variational Autoencoders, which model the latent safety constraints. Subsequently, we frame this as a Constrained Reward-Return Maximization problem, wherein the policy aims to optimize rewards while complying with the inferred latent safety constraints. This is achieved by training an encoder with a reward-Advantage Weighted Regression objective within the latent constraint space. Our methodology is supported by theoretical analysis, including bounds on policy performance and sample complexity. Extensive empirical evaluation on benchmark datasets, including challenging autonomous driving scenarios, demonstrates that our approach not only maintains safety compliance but also excels in cumulative reward optimization, surpassing existing methods. Additional visualizations provide further insights into the effectiveness and underlying mechanisms of our approach.