SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning

Researchers introduce SafeAdapt, a novel framework for updating reinforcement learning policies while maintaining provable safety guarantees across changing environments. The approach uses a 'Rashomon set' to identify safe parameter regions and projects policy updates onto this certified space, addressing the critical challenge of deploying RL agents in safety-critical applications where dynamics and objectives evolve over time.

SafeAdapt addresses a fundamental tension in deploying reinforcement learning systems: the need to adapt policies to new environments while guaranteeing they don't regress on previously learned safety constraints. Traditional RL update methods lack formal safety verification, typically validating safety only after deployment occurs. This research proposes an a priori certification approach that establishes safety before policy modifications take effect, a significant methodological advance for safety-critical applications like autonomous systems and robotics.

The Rashomon set concept provides the mathematical foundation for this approach by defining a bounded region in policy parameter space where all parameters provably satisfy safety constraints within demonstrated data distributions. By restricting policy updates to stay within this certified region, the framework ensures safety properties transfer across tasks without requiring post-hoc verification. Empirical validation on grid-world environments demonstrates that SafeAdapt prevents catastrophic forgetting of safety constraints that plague regularization-based baselines while maintaining strong adaptation performance.

This advancement carries substantial implications for the AI safety ecosystem and industries relying on autonomous systems. Formal safety guarantees reduce deployment risk and regulatory friction for safety-critical RL applications in healthcare, autonomous vehicles, and industrial control systems. The work validates that rigorous mathematical approaches can enable both adaptability and reliability simultaneously, rather than forcing tradeoffs between system flexibility and safety assurance.

Future research directions include scaling the Rashomon set approach to higher-dimensional policy spaces and real-world dynamics beyond grid-world environments. Integration with modern deep RL algorithms and validation on complex multi-task scenarios will determine whether this framework becomes a standard safety requirement for production deployments.

• →SafeAdapt provides formal, provable safety guarantees for RL policy updates a priori rather than verifying safety after deployment
• →The Rashomon set defines a certified region in policy space where all parameter choices maintain safety constraints on source tasks
• →Experimental results show SafeAdapt prevents catastrophic forgetting while enabling strong adaptation, outperforming regularization-based baselines
• →Framework applies to arbitrary RL algorithms by projecting their updates onto the safe parameter region
• →Advancement addresses critical deployment bottleneck for safety-critical autonomous systems requiring continuous policy adaptation