Machine Learning Street Talk (MLST) cover image

#030 Multi-Armed Bandits and Pure-Exploration (Wouter M. Koolen)

Machine Learning Street Talk (MLST)

00:00

Exploration and Causality in Reinforcement Learning

This chapter explores the significance of exploration in reinforcement learning, particularly in navigating environments without immediate rewards. It highlights the intricate relationship between causality and multi-armed bandits, examining how causal structures influence decision-making and the distinct challenges posed by adversarial settings. Additionally, the discussion focuses on the complexities of bandit optimization, the importance of sampling strategies, and the relevance of asymptotic optimality in algorithm design.

Transcript
Play full episode

The AI-powered Podcast Player

Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
App store bannerPlay store banner
Get the app