Reinforcement Learning Explained

Jonathan Sanito, Roland Fernandez, Adith Swaminathan, Kenneth Tran, Katja Hofmann, Matthew Hausknecht, Microsoft

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).

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

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.

edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

What will you learn

  • Reinforcement Learning Problem
  • Markov Decision Process
  • Bandits
  • Dynamic Programming
  • Temporal Difference Learning
  • Approximate Solution Methods
  • Policy Gradient and Actor Critic
  • RL that Works

Сессии:
  • 1 января 2019
Характеристики онлайн курса:
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  • Язык: Английский Gb

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