Reward Machines for Signal Temporal Logic
Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction. Control synthesis from STL specifications is of interest since manual controller design becomes infeasible as real-world systems grow in complexity. Moreover, many modern autonomous and AI-enabled systems lack accurate and complete system models, which makes optimization-based synthesis approaches unsuitable and motivates learning-based control. Prior work uses STL robustness scores as rewards in reinforcement learning (RL) to obtain control policies satisfying given specifications; however, robustne...
| Authors | Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai |
|---|---|
| Primary category | cs.AI |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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