Chapter 6: Learning in sequential decision-making under uncertainty
Manu K. Guptaa; Nandyala Hemachandrab; Shobhit Bhatnagarb a Department of Management Studies, IIT Roorkee, Roorkee, Indiab Industrial Engineering and Operations Research, IIT Bombay, Mumbai, India
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 ...
Get Artificial Intelligence and Machine Learning for EDGE Computing now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
