# Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems

arXiv: https://arxiv.org/abs/2009.09521 · HTML: https://mlsanity.com/paper/2009.09521/

## Key facts

- Cited by 12 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Quality Diversity Evolutionary Learning of Decision Trees](https://mlsanity.com/paper/2208.12758/) (2022)
- [Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications](https://mlsanity.com/paper/2207.01911/) (2022)
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- [Interpretable AI for policy-making in pandemics](https://mlsanity.com/paper/2204.04256/) (2022)
- [Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs](https://mlsanity.com/paper/2202.04943/) (2022)
- [Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems](https://mlsanity.com/paper/2201.12518/) (2022)
- [Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design](https://mlsanity.com/paper/2210.06825/) (2022)
- [Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations](https://mlsanity.com/paper/2211.15143/) (2022)
- [Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System](https://mlsanity.com/paper/2312.10472/) (2023)
- [ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies](https://mlsanity.com/paper/2607.07235/) (2026)
- [ConceptTree: Bringing Semantic Transparency to Black-Box Decision Making for Robotic Manipulation](https://mlsanity.com/paper/2607.17861/) (2026)

## Abstract (arXiv)

Black-box AI induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represented using a black-box AI are capable of efficiently executing the underlying control task and achieving optimal closed-loop performance, the developed control rules are often complex and neither interpretable nor explainable. In this paper, we use a recently proposed nonlinear decision-tree (NLDT) approach to find a hierarchical set of control rules in an attempt to maximize the open-loop performance for approximating and explaining the pre-trained black-box DRL (oracle) agent using the labelled state-action dataset. Recent advances in nonlinear optimization approaches using evolutionary computation facilitates finding a hierarchical set of nonlinear control rules as a function of state variables using a computationally fast bilevel optimization procedure at each node of the proposed NLDT. Additionally, we propose a re-optimization procedure for enhancing closed-loop performance of an already derived NLDT. We evaluate our proposed methodologies (open and closed-loop NLDTs) on different control problems having multiple discrete actions. In all these problems our proposed approach is able to find relatively simple and interpretable rules involving one to four non-linear terms per rule, while simultaneously achieving on par closed-loop performance when compared to a trained black-box DRL agent. A post-processing approach for simplifying the NLDT is also suggested. The obtained results are inspiring as they suggest the replacement of complicated black-box DRL policies involving thousands of parameters (making them non-interpretable) with relatively simple interpretable policies. Results are encouraging and motivating to pursue further applications of proposed approach in solving more complex control tasks.