Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning

Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning

Coppens, Youri (Vrije Universiteit Brussel)
Steckelmacher, Denis (Vrije Universiteit Brussel)
Jonker, C.M. (TU Delft Interactive Intelligence; Universiteit Leiden)
Nowe, A.S.P. (Vrije Universiteit Brussel)

Heintz, Fredrik (editor)
Milano, Michela (editor)
O’Sullivan, Barry (editor)

2021

Today’s advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algorithm, building on the CN2 rule mining algorithm, that distills the policy into a rule-based decision system. At the core of our approach is the fact that an RL process does not just learn a policy, a mapping from states to actions, but also produces extra meta-information, such as action values indicating the quality of alternative actions. This meta-information can indicate whether more than one action is near-optimal for a certain state. We extend CN2 to make it able to leverage knowledge about equally-good actions to distill the policy into fewer rules, increasing its interpretability by a person. Then, to ensure that the rules explain a valid, non-degenerate policy, we introduce a refinement algorithm that fine-tunes the rules to obtain good performance when executed in the environment. We demonstrate the applicability of our algorithm on the Mario AI benchmark, a complex task that requires modern reinforcement learning algorithms including neural networks. The explanations we produce capture the learned policy in only a few rules, that allow a person to understand what the black-box agent learned. Source code: https://gitlab.ai.vub.ac.be/yocoppen/svcn2.

Explainable AI
Inductive rule learning
Policy distillation
Reinforcement Learning

http://resolver.tudelft.nl/uuid:1ab03460-6575-4c7e-9aaf-8b5e3b307aa9

https://doi.org/10.1007/978-3-030-73959-1_15

Springer

2022-01-27

9783030739584

Trustworthy AI – Integrating Learning, Optimization and Reasoning - First International Workshop, TAILOR 2020, Revised Selected Papers

1st International Workshop on Trustworthy AI – Integrating Learning, Optimization and Reasoning, TAILOR 2020 held as a part of European Conference on Artificial Intelligence, ECAI 2020, 2020-09-04 → 2020-09-05, Virtual, Online

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12641 LNAI

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 Youri Coppens, Denis Steckelmacher, C.M. Jonker, A.S.P. Nowe