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Title: Policy Optimization with Linear Temporal Logic Constraints
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints. The language of LTL allows flexible description of tasks that may be unnatural to encode as a scalar cost function. We consider LTL-constrained PO as a systematic framework, decoupling task specification from policy selection, and as an alternative to the standard of cost shaping. With access to a generative model, we develop a model-based approach that enjoys a sample complexity analysis for guaranteeing both task satisfaction and cost optimality (through a reduction to a reachability problem). Empirically, our algorithm can achieve strong performance even in low-sample regimes.  more » « less
Award ID(s):
1918839
NSF-PAR ID:
10404359
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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