# Mo\"ET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

## TL;DR

Mo"ET is a novel mixture of expert trees model that enhances interpretability and safety in machine learning, especially in reinforcement learning, by enabling logical rule extraction and outperforming previous verifiable models.

## Contribution

Introduces Mo"ET, a mixture of decision tree experts with a generalized linear model gating function, and a hard thresholding variant Mo"ETH for improved interpretability and safety guarantees.

## Key findings

- Mo"ET outperforms decision tree-based methods in reinforcement learning tasks.
- Mo"ETH enables easy logical rule extraction for predictions.
- The models excel in real-world supervised problems, surpassing existing verifiable ML approaches.

## Abstract

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to expose the inner workings of the model in a human understandable form. We present Mo\"ET, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. Thanks to such gating function the model is more expressive than the standard decision tree. To support non-differentiable decision trees as experts, we formulate a novel training procedure. In addition, we introduce a hard thresholding version, Mo\"ETH, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, Mo\"ETH allows each prediction to be easily decomposed into a set of logical rules in a form which can be easily verified. While Mo\"ET is a general use model, we illustrate its power in the reinforcement learning setting. By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models. Moreover, we show that Mo\"ET can also be used in real-world supervised problems on which it outperforms other verifiable machine learning models.