Fuzzy Centered Explainable Network for Reinforcement Learning

Ou, L; Chang, YC; Wang, YK; Lin, CT

Fuzzy Centered Explainable Network for Reinforcement Learning

Ou, L Chang, YC Wang, YK Lin, CT

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2024, 32, (1), pp. 203-213
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Ou, L
dc.contributor.author	Chang, YC
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date.accessioned	2024-03-05T03:29:34Z
dc.date.available	2024-03-05T03:29:34Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2024, 32, (1), pp. 203-213
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/176121
dc.description.abstract	The explainability of reinforcement learning (RL) models has received vast amount of interest as its applications have widened. Most existing explainable RL models focus on improving the explainability of an agent's observations instead of the relationships between agent states and actions. This study presents a fuzzy centered explainable network (FCEN) for RL tasks to interpret the relationships between agent states and actions. The proposed FCEN leverages the interpretability of fuzzy neural networks to establish if-then rules and a generative model to visualize learned knowledge. Precisely, the FCEN includes if-then rules that formulate state-action mappings with human-understandable logic, such as the form 'IF Input is A THEN Output is B.' In addition, these rules connect with a generative model that concretizes the states into human-understandable patterns (figures). Our experimental results obtained on 4 Atari games show that the proposed FCEN can achieve a high level of performance in RL tasks and enormously boost the explainability of RL agents both globally and locally. In other words, the FCEN maintains a high-level explanation for the agent decision logic and the possibility of low-level analysis for each given observation sample. The explainability boost does not undermine reward learning performance, humans can even enhance the agent's performance with the provided explainability.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP210101093
dc.relation	United States Department of the NavyN629091912058
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2023.3295055
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4901 Applied mathematics
dc.title	Fuzzy Centered Explainable Network for Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-07-13T00:00:00+1000Z
dc.date.updated	2024-03-05T03:29:29Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	1

Abstract:

The explainability of reinforcement learning (RL) models has received vast amount of interest as its applications have widened. Most existing explainable RL models focus on improving the explainability of an agent's observations instead of the relationships between agent states and actions. This study presents a fuzzy centered explainable network (FCEN) for RL tasks to interpret the relationships between agent states and actions. The proposed FCEN leverages the interpretability of fuzzy neural networks to establish if-then rules and a generative model to visualize learned knowledge. Precisely, the FCEN includes if-then rules that formulate state-action mappings with human-understandable logic, such as the form 'IF Input is A THEN Output is B.' In addition, these rules connect with a generative model that concretizes the states into human-understandable patterns (figures). Our experimental results obtained on 4 Atari games show that the proposed FCEN can achieve a high level of performance in RL tasks and enormously boost the explainability of RL agents both globally and locally. In other words, the FCEN maintains a high-level explanation for the agent decision logic and the possibility of low-level analysis for each given observation sample. The explainability boost does not undermine reward learning performance, humans can even enhance the agent's performance with the provided explainability.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176121