Automata-Based Reward Machines for STL Control Synthesis
A recent paper on arXiv (2608.13625) presents a novel automata-driven strategy aimed at enhancing reinforcement learning (RL) for control synthesis derived from Signal Temporal Logic (STL) specifications. STL serves as a formal framework for articulating real-time characteristics of continuous observations, incorporating a quantitative robustness score to assess satisfaction. The task of synthesizing control from STL poses difficulties for intricate real-world systems, particularly since numerous contemporary autonomous and AI-driven systems operate without precise models, rendering optimization-based techniques ineffective and prompting the need for learning-based control. Earlier research employed STL robustness scores as rewards in RL; however, this approach suffers from state space expansion due to its reliance on execution history, especially with lengthy specifications containing nested temporal operators. The new method introduces an effective memory mechanism to mitigate this challenge.
Key facts
- Paper arXiv:2608.13625 introduces automata-based approach for STL control synthesis.
- STL is a formal language for real-time properties of real-valued observations.
- STL provides quantitative robustness score for monitoring satisfaction.
- Control synthesis from STL is of interest due to complexity of real-world systems.
- Many modern autonomous and AI-enabled systems lack accurate models.
- Optimization-based synthesis approaches are unsuitable for such systems.
- Prior work used STL robustness scores as rewards in reinforcement learning.
- Robustness depends on execution history, causing intractable state space expansion.
Entities
Institutions
- arXiv