Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Higher School of Economics

Introduction to Reinforcement Learning

Higher School of Economics via Coursera

This course may be unavailable.

Overview

Prepare for a new career with $100 off Coursera Plus
Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.
The goal of «Intro to Reinforcement learning» is in its name: introduce students to reinforcement learning – the prominent area of modern research in artificial intelligence. The reinforcement learning differs much from both supervised and unsupervised learning and is more about how humans learn in reality.
Students will learn from this course both theoretical core and recent practical RL methods. Most importantly, they will learn how to apply such methods to practical problems. In six weeks students will be guided through the basics of Reinforcement Learning (RL): we will talk about essential theory of RL, value-based methods (such as SARSA and Q-learning), policy based algorithms and methods, designed to solve the optimal exploration problem. In addition to algorithms and theory, during the course we will also present useful practical tips and tricks, needed for learning stabilization, and study how to apply the methods to large scale problems with deep neural networks.

Taught by

Pavel Shvechikov and Alexander Panin

Reviews

Start your review of Introduction to Reinforcement Learning

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.