Safe Reinforcement Learning Using Advantage-Based Intervention

TL;DR

This paper introduces SAILR, a safe reinforcement learning algorithm that ensures safety during training and deployment by using advantage-based interventions, outperforming existing methods in constraint adherence.

Contribution

SAILR is a novel safe RL algorithm that maintains safety during training through advantage-based interventions and guarantees safety and performance without intervention after training.

Findings

SAILR significantly reduces constraint violations during training.

SAILR converges to a high-performing, safe policy.

The method provides safety guarantees during both training and deployment.

Abstract

Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe policy after training, ensuring safety during training as well remains an open problem. A fundamental challenge is performing exploration while still satisfying constraints in an unknown Markov decision process (MDP). In this work, we address this problem for the chance-constrained setting. We propose a new algorithm, SAILR, that uses an intervention mechanism based on advantage functions to keep the agent safe throughout training and optimizes the agent's policy using off-the-shelf RL algorithms designed for unconstrained MDPs. Our method comes with strong guarantees on safety during both training and deployment (i.e., after training and without the intervention mechanism) and policy performance compared to the optimal safety-constrained policy. In our experiments, we show that SAILR violates constraints far less during training than standard safe RL and constrained MDP approaches and converges to a well-performing policy that can be deployed safely without intervention. Our code is available at https://github.com/nolanwagener/safe_rl.