SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning
<|reference_start|>SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning: 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. To the best of our knowledge, this is the first method, that allows a gradient-based end-to-end learning of interpretable, axis-aligned decision trees within existing 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<|reference_end|>
@article{marton2024sympol:,
    title={SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning},
    author={Sascha Marton, Tim Grams, Florian Vogt, Stefan L"udtke, Christian
  Bartelt, Heiner Stuckenschmidt},
    journal={arXiv preprint arXiv:2408.08761},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08761},
    primaryClass={cs.LG}
}