Foundations of Reinforcement Learning and Interactive Decision Making
Machine Learning
2023-12-29 v1 Optimization and Control
Statistics Theory
Machine Learning
Statistics Theory
Abstract
These lecture notes give a statistical perspective on the foundations of reinforcement learning and interactive decision making. We present a unifying framework for addressing the exploration-exploitation dilemma using frequentist and Bayesian approaches, with connections and parallels between supervised learning/estimation and decision making as an overarching theme. Special attention is paid to function approximation and flexible model classes such as neural networks. Topics covered include multi-armed and contextual bandits, structured bandits, and reinforcement learning with high-dimensional feedback.
Cite
@article{arxiv.2312.16730,
title = {Foundations of Reinforcement Learning and Interactive Decision Making},
author = {Dylan J. Foster and Alexander Rakhlin},
journal= {arXiv preprint arXiv:2312.16730},
year = {2023}
}