Title: Select the Right Agent: Data-Driven Online Model Selection in Reinforcement Learning
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Model Selection

Abstract: We study the problem of online model selection in reinforcement learning, where the selector has access to a class of reinforcement learning agents and learns to adaptively select the agent with the right configuration. Our goal is to establish the improved efficiency and performance gains achieved by integrating data-driven model selection methods into reinforcement learning training procedures.  We examine the theoretical characterizations that are effective for identifying the right configuration in practice, and address three practical criteria from a theoretical perspective: 1) Efficient resource allocation, 2) Stabilized training, 3) Adaptation under non-stationary dynamics. Our theoretical results are accompanied by empirical evidence from various model selection tasks in reinforcement learning, including neural architecture selection, step-size selection, and self-model selection.