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SMC-ES: Automated synthesis of formally verified co... | AI Research

Key Takeaways

  • SMC-ES: Automated synthesis of formally verified control policies This paper addresses a critical challenge in modern robotics and autonomous systems: how to...
  • The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.e., policies) that are not only performant but also provably safe and robust.
  • To bridge this gap, we propose a novel simulation-based methodology to automatically synthesize policies with formal guarantees regarding performance, safety, and robustness specifications.
  • We demonstrate the feasibility of our approach by developing SMC-ES, an algorithm that integrates Evolutionary Strategies with Statistical Model Checking-based verification.
  • We evaluate SMC-ES on a suite of continuous control tasks using Gymnasium and Safety Gymnasium testbeds.
Paper AbstractExpand

The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.e., policies) that are not only performant but also provably safe and robust. While learning-based methodologies such as Reinforcement Learning offer flexible and scalable approaches to automatically synthesize such controllers, they typically lack the formal guarantees necessary for safe deployment. To bridge this gap, we propose a novel simulation-based methodology to automatically synthesize policies with formal guarantees regarding performance, safety, and robustness specifications. Specifically, given a set of properties to verify, a confidence parameter $\delta$ and an allowable failure probability $\varepsilon$, our method guarantees that the synthesized policy comes with a certificate: with confidence at least $1 - \delta$, the probability of encountering a scenario where the given properties are violated is at most $\varepsilon$. We demonstrate the feasibility of our approach by developing SMC-ES, an algorithm that integrates Evolutionary Strategies with Statistical Model Checking-based verification. We evaluate SMC-ES on a suite of continuous control tasks using Gymnasium and Safety Gymnasium testbeds. Results show that, at the price of a sustainable increase in computational cost, our algorithm provides formal guarantees regarding performance, safety, and robustness specifications, while performing competitively against leading model-free Deep Reinforcement Learning (DRL) and Safe-DRL baselines.

SMC-ES: Automated synthesis of formally verified control policies
This paper addresses a critical challenge in modern robotics and autonomous systems: how to create control policies that are both high-performing and guaranteed to be safe. While modern learning-based methods like Reinforcement Learning (RL) are excellent at solving complex tasks, they often lack the formal safety guarantees required for real-world, high-stakes environments. The authors introduce a new methodology that bridges this gap by combining simulation-based learning with formal verification, ensuring that a policy is not just effective, but also provably robust against failure.

Bridging Learning and Formal Verification

The core innovation of this work is a framework that provides a "certificate" for control policies. When a policy is synthesized, it comes with a specific guarantee: given a confidence level and an allowable failure probability, the system ensures that the chance of violating safety or performance requirements remains below a set threshold. This allows developers to move beyond simple empirical testing and toward a more rigorous, mathematically grounded approach to deploying autonomous systems.

How SMC-ES Works

The authors developed an algorithm called SMC-ES, which integrates two distinct techniques: Evolutionary Strategies (ES) and Statistical Model Checking (SMC). The process functions as an iterative loop:

  • Evolutionary Strategy: An optimization algorithm searches for the best control policy by evolving a population of potential solutions. This approach is highly parallelizable and does not require the system to be differentiable.

  • Statistical Model Checking: Once a candidate policy is generated, SMC acts as a verifier. It tests the policy against specific safety and performance requirements.

  • Refinement: If the SMC process identifies a scenario where the policy fails, it generates a "counterexample." This information is fed back into the optimization loop to refine the policy, ensuring that future iterations avoid those specific failure points.

Performance and Robustness

The authors tested SMC-ES on various continuous control tasks, including the Humanoid environment, which is known for being particularly difficult. The results show that SMC-ES is competitive with leading Deep Reinforcement Learning (DRL) and Safe-DRL baselines in terms of performance. Notably, the synthesized policies were able to guarantee that the agent would never fall, regardless of its initial state. Furthermore, the researchers tested these policies under synthetic noise conditions to simulate real-world disturbances, finding that the policies maintained their performance and safety guarantees even when faced with perturbations.

Considerations for Implementation

While SMC-ES provides significant advantages in safety and reliability, the authors acknowledge that it requires a higher computational budget compared to standard DRL algorithms. Achieving formal guarantees involves rigorous verification steps that increase the overall time and resources needed for training. However, the authors argue that this is a sustainable and necessary trade-off for applications where safety and worst-case behavior are more critical than simple average-case utility.

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