Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning

Austin Jones; Derya Aksaray; Zhaodan Kong; Mac Schwager; Calin Belta

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arXiv · 1510.06460

Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning

Austin Jones·Derya Aksaray·Zhaodan Kong·Mac Schwager·Calin Belta

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Abstract

We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally layered task given as a signal temporal logic formula. We represent the system as a Markov decision process in which the states are built from a partition of the state space and the transition probabilities are unknown. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given formula and to maximize the average expected robustness, i.e., a measure of how strongly the formula is satisfied. We demonstrate via a pair of robot navigation simulation case studies that reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness.

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BibTeX RIS

Austin Jones, Derya Aksaray, Zhaodan Kong, Mac Schwager, Calin Belta. 2015-10-22. Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning. https://arxiv.org/abs/1510.06460

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