2024/11/18 by Juan Cardenas-Cartagena, Cardenas-Cartagena, Juan, Massimiliano Falzari +5
Computer Science · Engineering · Physics and Astronomy · #Advanced Control Systems Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.2411.11457
openalex publication_date 2024/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Model-Free Reinforcement Learning (RL) algorithms either learn how to map states to expected rewards or search for policies that can maximize a certain performance function. Model-Based algorithms instead, aim to learn an approximation of the underlying model of the RL environment and then use it in combination with planning algorithms. Upside-Down Reinforcement Learning (UDRL) is a novel learning paradigm that aims to learn how to predict actions from states and desired commands. This task is formulated as a Supervised Learning problem and has successfully been tackled by Neural Networks (NNs). In this paper, we investigate whether function approximation algorithms other than NNs can also be used within a UDRL framework. Our experiments, performed over several popular optimal control benchmarks, show that tree-based methods like Random Forests and Extremely Randomized Trees can perform just as well as NNs with the significant benefit of resulting in policies that are inherently more interpretable than NNs, therefore paving the way for more transparent, safe, and robust RL.