Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

Sascha Marton, Tim Grams, Florian Vogt, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

Keywords: Symbolic Reinforcement Learning, Interpretable Reinforcement Learning, Reinforcement Learning, Decision Trees, Policy Gradient, Proximal Policy Optimization

TL;DR: SYMPOL is a novel method for symbolic RL that enables end-to-end gradient-based learning of interpretable, axis-aligned decision trees, combining policy gradient optimization with symbolic decision-making

Abstract:

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

Primary Area: reinforcement learning

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Submission Number: 1507