Foundations of Reinforcement Learning and Interactive Decision Making

תקציר מקורי באנגליתarXiv:2312.16730v2 Announce Type: replace Abstract: Interactive decision making is the problem of learning to act well in an unknown environment, using the data that one's own actions generate to continuously improve, and arises in situations ranging from online platforms and robotics to medical treatments. This monograph gives a statistical perspective on algorithm design and complexity for interactive decision making, building from multi-armed bandits through contextual and structured bandits to reinforcement learning with function approximation within a single, unified framework. Special attention is paid to function approximation and flexible models such as neural networks, and to the connection between supervised learning and decision making: the reader will learn how to turn any supe