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Mitigating information loss in tree-based reinforcement learning via direct optimization
Marton, Sascha
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Grams, Tim
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Vogt, Florian
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Lüdtke, Stefan
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Bartelt, Christian
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Stuckenschmidt, Heiner
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URL:
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https://openreview.net/forum?id=qpXctF2aLZ
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URN:
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urn:nbn:de:bsz:180-madoc-695066
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Document Type:
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Conference or workshop publication
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Year of publication Online:
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2025
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Book title:
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The Thirteens International Conference on Learning Representations
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Conference title:
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ICLR 2025, The Thirteenth International Conference on Learning Representations
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Location of the conference venue:
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Singapur, Singapore
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Date of the conference:
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24.-28.04.2025
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Publishing house:
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OpenReview.net
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Related URLs:
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Publication language:
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English
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Institution:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Pre-existing license:
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 Creative Commons Attribution 4.0 International (CC BY 4.0)
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Subject:
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004 Computer science, internet
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Keywords (English):
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symbolic reinforcement learning , interpretable reinforcement learning , reinforcement learning , decision trees , policy gradient , proximal policy optimization
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Abstract:
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Reinforcement learning (RL) has seen significant success across various domains, but its adoption is often limited by the black-box nature of neural network policies, making them difficult to interpret. In contrast, symbolic policies allow representing decision-making strategies in a compact and interpretable way. However, learning symbolic policies directly within on-policy methods remains challenging. In this paper, we introduce SYMPOL, a novel method for SYMbolic tree-based on-POLicy RL. SYMPOL employs a tree-based model integrated with a policy gradient method, enabling the agent to learn and adapt its actions while maintaining a high level of interpretability. We evaluate SYMPOL on a set of benchmark RL tasks, demonstrating its superiority over alternative tree-based RL approaches in terms of performance and interpretability. Unlike existing methods, it enables gradient-based, end-to-end learning of interpretable, axis-aligned decision trees within standard on-policy RL algorithms. Therefore, SYMPOL can become the foundation for a new class of interpretable RL based on decision trees. Our implementation is available under: https://github.com/s-marton/sympol
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 Search Authors in
BASE:
Marton, Sascha
;
Grams, Tim
;
Vogt, Florian
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
Google Scholar:
Marton, Sascha
;
Grams, Tim
;
Vogt, Florian
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
ORCID:
Marton, Sascha  ORCID: 0000-0001-8151-9223 ; Grams, Tim ; Vogt, Florian ; Lüdtke, Stefan ; Bartelt, Christian ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859
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