Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning Chapter uri icon

Overview

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 $$omega $$; ��; -regular objectives in RL.

publication date

  • January 1, 2023

has restriction

  • hybrid

Date in CU Experts

  • April 26, 2023 12:35 PM

Full Author List

  • Hahn EM; Perez M; Schewe S; Somenzi F; Trivedi A; Wojtczak D

author count

  • 6

Other Profiles

International Standard Book Number (ISBN) 13

  • 9783031308222

Additional Document Info

start page

  • 527

end page

  • 545