Reinforcement Learning Explained (edX)

Reinforcement Learning Explained (edX)
Course Auditing
Categories
Effort
Certification
Languages
Misc

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

Reinforcement Learning Explained (edX)
Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).

Class Deals by MOOC List - Click here and see edX's Active Discounts, Deals, and Promo Codes.

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

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.

In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole.

You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.

This course is part of the Microsoft Professional Program in Artificial Intelligence.


What you'll learn

- Reinforcement Learning Problem

- Markov Decision Process

- Bandits

- Dynamic Programming

- Temporal Difference Learning

- Approximate Solution Methods

- Policy Gradient and Actor Critic

- RL that Works



0
No votes yet

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