2023/11/30 by Bianca Marin Moreno, Moreno, Bianca Marin, Margaux Brégère +5 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Reinforcement Learning in Robotics #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2311.18346
openalex publication_date 2023/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them. These include reinforcement learning, imitation learning, among others. This more general paradigm is called the Concave Utility Reinforcement Learning problem (CURL). Since CURL invalidates classical Bellman equations, it requires new algorithms. We introduce MD-CURL, a new algorithm for CURL in a finite horizon Markov decision process. MD-CURL is inspired by mirror descent and uses a non-standard regularization to achieve convergence guarantees and a simple closed-form solution, eliminating the need for computationally expensive projection steps typically found in mirror descent approaches. We then extend CURL to an online learning scenario and present Greedy MD-CURL, a new method adapting MD-CURL to an online, episode-based setting with partially unknown dynamics. Like MD-CURL, the online version Greedy MD-CURL benefits from low computational complexity, while guaranteeing sub-linear or even logarithmic regret, depending on the level of information available on the underlying dynamics.