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This content will become publicly available on April 22, 2024

Title: Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning.
Mungojerrie 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-regular objectives, and an internal probabilistic model checker for omega-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-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.  more » « less
Award ID(s):
2009022 2146563
NSF-PAR ID:
10417929
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Sankaranarayanan, S.; Sharygina, N.
Date Published:
Journal Name:
Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2023.
Volume:
13993
Page Range / eLocation ID:
527–545
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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