Understanding Algorithms for Reinforcement Learning

Reinforcement learning is a type of machine learning which allows decision makers to operate in an unknown environment. In the world of self-driving cars and exploring robots, RL is an important field of study for any student of machine learning.
Course info
Level
Beginner
Updated
Jul 6, 2018
Duration
2h 8m
Table of contents
Description
Course info
Level
Beginner
Updated
Jul 6, 2018
Duration
2h 8m
Description

Traditional machine learning algorithms are used for predictions and classification. Reinforcement learning is about training agents to take decisions to maximize cumulative rewards. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. First, you'll discover the objective of reinforcement learning; to find an optimal policy which allows agents to make the right decisions to maximize long-term rewards. You'll study how to model the environment so that RL algorithms are computationally tractable. Next, you'll explore dynamic programming, an important technique used to cache intermediate results which simplify the computation of complex problems. You'll understand and implement policy search techniques such as temporal difference learning (Q-learning) and SARSA which help converge on to an optimal policy for your RL algorithm. Finally, you'll build reinforcement learning platforms which allow study, prototyping, and development of policies, as well as work with both Q-learning and SARSA techniques on OpenAI Gym. By the end of this course, you should have a solid understanding of reinforcement learning techniques, Q-learning and SARSA and be able to implement basic RL algorithms.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name Janani Ravi, and welcome to this course on Understanding Algorithms for Reinforcement Learning. A little about myself, I have a Masters degree in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. In this course, you will learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. We'll start off by understanding the objective of reinforcement learning to find an optimal policy, which allows agents to make the right decisions to maximize long-term rewards. RL has a wide variety of use cases such as optimizing trucking routes to conserve fuel, finding the best moves to beat an opponent in chess. We'll study how to model the environment using Markov decision processes so that RL algorithms are computationally tractable. We'll then study dynamic programming, an important technique used to memoize intermediate results, which simplifies the computation of complex problems. We'll understand and implement policy search techniques such as temporal difference learning, also called Q-learning, and SARSA, which help converge to an optimal policy for our RL algorithm. We'll then study reinforcement learning platforms, which allow us to study prototype and develop our policies. We'll work with both Q-learning and SARSA techniques on OpenAI Gym. At the end of this course, you should have a solid understanding of reinforcement learning techniques, Q-learning and SARSA, and be able to implement basic RL algorithms.