Poster
Policy Optimization with Linear Temporal Logic Constraints
Cameron Voloshin · Hoang Le · Swarat Chaudhuri · Yisong Yue
Hall J (level 1) #719
Keywords: [ RL ] [ Constrained ] [ LTL ] [ Reinforcement Learning ] [ Optimization ] [ Policy ] [ linear temporal logic ] [ learning ]
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.