# CDT: Cascading Decision Trees for Explainable Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.07553 · HTML: https://mlsanity.com/paper/2011.07553/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning](https://mlsanity.com/paper/2309.16984/) (2023)
- [POETREE: Interpretable Policy Learning with Adaptive Decision Trees](https://mlsanity.com/paper/2203.08057/) (2022)
- [MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees](https://mlsanity.com/paper/2209.07225/) (2022)
- [Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications](https://mlsanity.com/paper/2207.01911/) (2022)
- [Vanilla Gradient Descent for Oblique Decision Trees](https://mlsanity.com/paper/2408.09135/) (2024)
- [Synthesizing Programmatic Policies with Actor-Critic Algorithms and ReLU Networks](https://mlsanity.com/paper/2308.02729/) (2023)
- [SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks](https://mlsanity.com/paper/2411.12173/) (2024)
- [Solving Offline Reinforcement Learning with Decision Tree Regression](https://mlsanity.com/paper/2401.11630/) (2024)
- [RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space](https://mlsanity.com/paper/2410.16517/) (2024)
- [Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models](https://mlsanity.com/paper/2206.04661/) (2022)
- [Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networks](https://mlsanity.com/paper/2402.05307/) (2024)
- [Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?](https://mlsanity.com/paper/2306.13004/) (2023)
- [Approximation-Free Differentiable Oblique Decision Trees](https://mlsanity.com/paper/2605.07837/) (2026)
- [Reinforcement Learning: From Algorithms To Foundation Models](https://mlsanity.com/paper/2607.17560/) (2026)
- [Explainable Reinforcement Learning for assisting Air Traffic Controllers](https://mlsanity.com/paper/2607.22525/) (2026)

## Abstract (arXiv)

Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to several factors, one being the complexity of explaining neural networks decisions. Recently, a group of works have used decision-tree-based models to learn explainable policies. Soft decision trees (SDTs) and discretized differentiable decision trees (DDTs) have been demonstrated to achieve both good performance and share the benefit of having explainable policies. In this work, we further improve the results for tree-based explainable RL in both performance and explainability. Our proposal, Cascading Decision Trees (CDTs) apply representation learning on the decision path to allow richer expressivity. Empirical results show that in both situations, where CDTs are used as policy function approximators or as imitation learners to explain black-box policies, CDTs can achieve better performances with more succinct and explainable models than SDTs. As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.