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The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end.By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or

4.6
30 reviews

Created by: Martha White

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Course Description

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent's own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment's dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.By the end of this course you will be able to:- Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience- Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model- Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions- Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control- Understand planning with simulated experience (as opposed to classic planning strategies)- Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna
Read More product length iconTime: 19h

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Instructor Details

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Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, Faculty of Science. Her research focus is on developing algorithms for agents continually learning on streams of data, with an emphasis on representation learning and reinforcement learning. Martha is a PI of AMII---the Alberta Machine Intelligence Institute and a director of RLAI---the Reinforcement Learning and Artificial Intelligence Lab at the University of Alberta. She enjoys soccer, the outdoors, cooking and especially reading sci-fi.

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Reviews

4.6

30 reviews in total

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By Rashid P on 12-Nov-19

Best RL course ever done

By John H on 10-Nov-19

It was good.

By AhmadrezaSheibanirad on 10-Nov-19

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

By Chan Y F on 4-Nov-19

The video content is not elaborated enough, need to read the book and search on the web to understand the idea

By David P on 3-Nov-19

Really a wonderful course! Very professional and high level.

By Alberto H on 28-Oct-19

A great step towards the acquisition of basic and medium complexity RL concepts with a nice balance between theory and practice, similar to the first one.[Note: the course requires mastering the concepts of the first one in the specialization, so don't start here unless you're sure you master its contents.]

By Max C on 24-Oct-19

Some of the programming homeworks were difficult to debug due to the feedback from autograder being unhelpful.

By Sriram R on 21-Oct-19

Well done mix of theory and practice!

By Wang G on 19-Oct-19

Very Nice Explanation and Assignment! Look forward the next 2 courses in this specialization!

By Navid H on 16-Oct-19

definitely interesting subjects, but I do not like the teaching method. Very mechanic and dull, with not enough connection to the real world

By Ignacio O on 13-Oct-19

Great, informative and very interesting course.

By David C on 10-Oct-19

A very good course. The lectures are brief and provide a quick overview of the topics. The quizzes require more in-depth reading to pass (covering material not discussed in the lectures) and the projects are difficult but rewarding and really help to cement the information. My only suggestion would be to lengthen the lectures to provide more discussion on the topics.

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Overall Score : 92 / 100

Sample-based Learning Methods
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  • Video Duration: icon 19h
  • Price: Free
  • Provider: icon Coursera
  • University: icon IBM
  • Language: icon English
  • Certificate: icon Yes

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