Options
Learning in sequential decision-making under uncertainty
Journal
Artificial Intelligence and Machine Learning for EDGE Computing
Date Issued
2022-01-01
Author(s)
Gupta, Manu K.
Hemachandra, Nandyala
Bhatnagar, Shobhit
Abstract
Reinforcement learning (RL) is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic rewards with its past actions. With numerous successful applications in business intelligence, health care, finance, and gaming, the RL framework is ideal for sequential decision-making in unknown environments with large amounts of data. Multiarmed bandits are the simplest form of reinforcement learning. This chapter provides a systematic bridge between RL and multiarmed bandits. We also discuss the state-of-the-art results in nonstationary environment, which is practical in several real-life applications.
Subjects