Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning

Alper Kamil Bozkurt; Yu Wang; Michael M. Zavlanos; Miroslav Pajic

doi:10.1109/icra40945.2020.9196796

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arXiv · 1909.07299

Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning

Alper Kamil Bozkurt·Yu Wang·Michael M. Zavlanos·Miroslav Pajic

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Abstract

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.

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BibTeX RIS

Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos, Miroslav Pajic. 2019-09-16. Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning. https://doi.org/10.1109/icra40945.2020.9196796

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