{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/moet-interpretable-and-verifiable","title":"MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning","arxiv_id":"1906.06717","date":"2019-06-16","proceeding":null,"authors":["Marko Vasic","Andrija Petrovic","Kaiyuan Wang","Mladen Nikolic","Rishabh Singh","Sarfraz Khurshid"],"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.","url_abs":"https://arxiv.org/abs/1906.06717v4","url_pdf":"https://arxiv.org/pdf/1906.06717v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"moet-interpretable-and-verifiable","repo_url":"https://github.com/marko-vasic/moet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"moet-interpretable-and-verifiable","repo_url":"https://github.com/simsal0r/mixture-of-decision-trees","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"game-of-go","task_name":"Game of Go"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"task_slug":"mixture-of-experts","task_name":"Mixture-of-Experts"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.06717","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}