Lecture

Reinforcement Learning: Q-Learning

Description

This lecture introduces the concept of reinforcement learning, focusing on Q-Learning as a model-free method for agents to learn by trial-and-error. It covers the exploration-exploitation trade-off, action values, and policies. The lecture also discusses the convergence properties of Q-Learning, the importance of human-compatible AI, and the impact of algorithms on society.

Instructor

esse labore pariatur et

Excepteur eiusmod ipsum exercitation quis eiusmod cupidatat elit nulla. Cupidatat pariatur nostrud exercitation duis et officia dolor proident minim aute. Dolore qui eu ad nulla consequat exercitation. Incididunt dolore officia qui cupidatat cillum. Culpa officia quis minim laboris voluptate non et.

Official source

https://mediaspace.epfl.ch/media/0_38vn8zck

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Information engineering

Machine learning: Reinforcement learning

Related lectures (84)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.