Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning

Abstract

AbstractMungojerrie is an extensible tool that provides a framework to translate linear-time objectives into reward for reinforcement learning (RL). The tool provides convergent RL algorithms for stochastic games, reference implementations of existing reward translations for $$\omega $$ ω -regular objectives, and an internal probabilistic model checker for $$\omega $$ ω -regular objectives. This functionality is modular and operates on shared data structures, which enables fast development of new translation techniques. Mungojerrie supports finite models specified in PRISM and $$\omega $$ ω -automata specified in the HOA format, with an integrated command line interface to external linear temporal logic translators. Mungojerrie is distributed with a set of benchmarks for $$\omega $$ ω -regular objectives in RL.