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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
Reward Machines for Signal Temporal Logic
TL;DR
A novel automata-based approach for reinforcement learning (RL) is introduced, enhancing control policy learning from Signal Temporal Logic (STL) specifications. This method achieves higher robustness scores and satisfaction rates compared to existing robustness-based reward methods.
✦ Why It Matters
Implement this automata-based approach to improve your RL models for real-time system control today.
Key Takeaways
- Explore automata-based methods for STL in your RL projects.
- Utilize the new framework to handle complex temporal specifications.
- Focus on improving robustness scores in your control policies.
- Consider learning-based control for systems lacking complete models.
How It Works
The proposed method constructs a timed alternating automaton from STL specifications, which allows for efficient state management. By integrating automaton locations and clock valuations into the state space, it provides a structured way to derive rewards based on the automaton's acceptance conditions.
This approach mitigates the state space expansion issues associated with traditional robustness-based reward methods, enabling effective learning of control policies.