Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controllers for discrete systems, complex continuous dynamics still pose a challenge. In particular, when the relationships between variables take more complex forms, such as polynomials, they cannot be obtained using the available DT learning procedures. In contrast, support vector machines provide a more powerful representation, capable of discovering many such relationships, but not in an explainable form. Therefore, we suggest to combine the two frameworks in order to obtain an understandable representation over richer, domain-relevant algebraic predicates. We demonstrate and evaluate the proposed method experimentally on established benchmarks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts
Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable. The rece…
Decision MakingdtControl: Decision Tree Learning Algorithms for Controller Representation
Decision tree learning is a popular classification technique most commonly used in machine learning applications. Recent work has shown that decision trees can be used to represent provably-correct controllers concisely.…
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers
Demand-side flexibility is gaining importance as a crucial element in the energy transition process. Accounting for about 25% of final energy consumption globally, the residential sector is an important (potential) sourc…
energy managementReinforcement Learning (RL)Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
With the ongoing energy transition, demand-side flexibility has become an important aspect of the modern power grid for providing grid support and allowing further integration of sustainable energy sources. Besides tradi…
energy managementManagementreinforcement-learningdtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees
Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many c…