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) source of energy flexibility. However, unlocking this flexibility requires developing a control framework that (1) easily scales across different houses, (2) is easy to maintain, and (3) is simple to understand for end-users. A potential control framework for such a task is data-driven control, specifically model-free reinforcement learning (RL). Such RL-based controllers learn a good control policy by interacting with their environment, learning purely based on data and with minimal human intervention. Yet, they lack explainability, which hampers user acceptance. Moreover, limited hardware capabilities of residential assets forms a hurdle (e.g., using deep neural networks). To overcome both those challenges, we propose a novel method to obtain explainable RL policies by using differentiable decision trees. Using a policy distillation approach, we train these differentiable decision trees to mimic standard RL-based controllers, leading to a decision tree-based control policy that is data-driven and easy to explain. As a proof-of-concept, we examine the performance and explainability of our proposed approach in a battery-based home energy management system to reduce energy costs. For this use case, we show that our proposed approach can outperform baseline rule-based policies by about 20-25%, while providing simple, explainable control policies. We further compare these explainable policies with standard RL policies and examine the performance trade-offs associated with this increased explainability.
Code (0)
등록된 구현이 없습니다.
Tasks
energy managementReinforcement Learning (RL)Similar Papers 제목 키워드 기반
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
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 …
Deep Reinforcement LearningExplainable ModelsImitation Learningreinforcement-learning+3DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture. In contrast to existing black-box GNNs and post-hoc explanation methods, the reasoning of DT+GNN can be inspected at every step. …
Decision MakingGraph Neural NetworkInterpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of …
Decision MakingDeep Reinforcement Learningenergy managementManagement+2SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks
Deep reinforcement learning (DRL) has achieved remarkable success in various research domains. However, its reliance on neural networks results in a lack of transparency, which limits its practical applications. To achie…
continuous-controlContinuous ControlDecision MakingDeep Reinforcement LearningExplainable 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-learning