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- W1503046297 abstract "We consider a problem on the synthesis of reactive controllers that optimize some a priori unknown performance criterion while interacting with an uncontrolled environment such that the system satisfies a given temporal logic specification. We decouple the problem into two subproblems. First, we extract a (maximally) permissive strategy for the system, which encodes multiple (possibly all) ways in which the system can react to the adversarial environment and satisfy the specifications. Then, we quantify the a priori unknown performance criterion as a (still unknown) reward function and compute an optimal strategy for the system within the operating envelope allowed by the permissive strategy by using the so-called maximin-Q learning algorithm. We establish both correctness (with respect to the temporal logic specifications) and optimality (with respect to the a priori unknown performance criterion) of this two-step technique for a fragment of temporal logic specifications. For specifications beyond this fragment, correctness can still be preserved, but the learned strategy may be sub-optimal. We present an algorithm to the overall problem, and demonstrate its use and computational requirements on a set of robot motion planning examples." @default.
- W1503046297 created "2016-06-24" @default.
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- W1503046297 date "2015-03-05" @default.
- W1503046297 modified "2023-09-27" @default.
- W1503046297 title "Correct-by-synthesis reinforcement learning with temporal logic constraints" @default.
- W1503046297 doi "https://doi.org/10.48550/arxiv.1503.01793" @default.
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