A Framework for Transforming Specifications in Reinforcement Learning

Abstract

Reactive synthesis algorithms allow automatic construction of policies to control an environment modeled as a Markov Decision Process (MDP) that are optimal with respect to high-level temporal logic specifications. However, they assume that the MDP model is known a priori. Reinforcement Learning (RL) algorithms, in contrast, are designed to learn an optimal policy when the transition probabilities of the MDP are unknown, but require the user to associate local rewards with transitions. The appeal of high-level temporal logic specifications has motivated research to develop RL algorithms for synthesis of policies from specifications. To understand the techniques, and nuanced variations in their theoretical guarantees, in the growing body of resulting literature, we develop a formal framework for defining transformations among RL tasks with different forms of objectives. We define the notion of a sampling-based reduction to transform a given MDP into another one which can be simulated even when the transition probabilities of the original MDP are unknown. We formalize the notions of preservation of optimal policies, convergence, and robustness of such reductions. We then use our framework to restate known results, establish new results to fill in some gaps, and identify open problems. In particular, we show that certain kinds of reductions from LTL specifications to reward-based ones do not exist, and prove the non-existence of RL algorithms with PAC-MDP guarantees for safety specifications.