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 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.

Authors

• Cameron Voloshin
• Hoang Le
• Swarat Chaudhuri University of Texas at Austin & Google Deepmind
• Yisong Yue

Keywords

No keywords are indexed for this paper.