Provably Safe Reinforcement Learning from Analytic Gradients

2025-06-02

AI Summary

Existing provably safe reinforcement learning (RL) methods for safety-critical autonomous robotics primarily target sample-based RL, leaving high-performance, sample-efficient analytic-gradient RL algorithms—such as Proximal Policy Optimization (PPO)—without training-time safety guarantees, thereby exacerbating the safety gap between simulation and reality. Method: This paper introduces the first provably safe training-time framework for analytic-gradient RL. It proposes a differentiable safety module integrating state-action space projection mapping, gradient redefinition, and differentiable physics simulation to enable end-to-end co-optimization of safety constraints and policy learning. Contribution/Results: Evaluated on canonical control benchmarks, the approach achieves zero safety violations throughout training while matching the performance of unconstrained baselines. It effectively bridges the longstanding trade-off between safety assurance and policy performance, enabling safer deployment of gradient-based RL in real-world robotic systems.

Technology Category

Natural Language Processing: Safety and RobustnessIntelligent Robots: Learning & Optimization for ROBMachine Learning: Adversarial Learning & Robustness

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSecurity and Privacy: Data transparency and provenanceResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Deploying autonomous robots in safety-critical applications requires safety guarantees. Provably safe reinforcement learning is an active field of research which aims to provide such guarantees using safeguards. These safeguards should be integrated during training to prevent a large sim-to-real gap. While there are several approaches for safeguarding sampling-based reinforcement learning, analytic gradient-based reinforcement learning often achieves superior performance and sample efficiency. However, there is no safeguarding approach for this learning paradigm yet. Our work addresses this gap by developing the first effective safeguard for analytic gradient-based reinforcement learning. We analyse existing, differentiable safeguards, adapt them through modified mappings and gradient formulations, and integrate them with a state-of-the-art learning algorithm and a differentiable simulation. We evaluate how different safeguards affect policy optimisation using numerical experiments on two classical control tasks. The results demonstrate safeguarded training without compromising performance.

Problem

• Ensuring safety in analytic gradient-based reinforcement learning
• Developing safeguards for differentiable simulation and learning
• Integrating safety without compromising policy optimization performance

Innovation

• First safeguard for gradient-based reinforcement learning
• Modified mappings and gradient formulations
• Integration with state-of-the-art learning algorithm