التفاصيل البيبلوغرافية
| العنوان: |
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning |
| المؤلفون: |
Bozkurt, Alper Kamil, Wang, Yu, Zavlanos, Michael M., Pajic, Miroslav |
| سنة النشر: |
2019 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: |
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP). Specifically, we learn a policy that maximizes the probability of satisfying the LTL formula without learning the transition probabilities. We introduce a novel rewarding and path-dependent discounting mechanism based on the LTL formula such that (i) an optimal policy maximizing the total discounted reward effectively maximizes the probabilities of satisfying LTL objectives, and (ii) a model-free RL algorithm using these rewards and discount factors is guaranteed to converge to such policy. Finally, we illustrate the applicability of our RL-based synthesis approach on two motion planning case studies. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/1909.07299 |
| رقم الانضمام: |
edsarx.1909.07299 |
| قاعدة البيانات: |
arXiv |