Sample-based Learning Methods

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

( 30 Reviews )

Created by: Martha White

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Overall Score : 92 / 100

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

Time: 19h

Instructor Details

Martha White

4.6 Rating
30 Reviews

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.

Reviews

4.6

30 total reviews

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By Miguel A C N on 23-Sep-19

Rating 4.3 stars Ã¢Â€Â“ so far (first two classes combined)
Lectures: 4.0stars
Quizes: 4.0stars
Programming assignments: 4.5stars
Book (Sutton and Barto): 4.5stars
In the spectrum from the theoretical to practical where you have, very roughly,...
(1) Ã¢Â€ÂœWhyÃ¢Â€Â: Why you are doing what you are doing
(2) Ã¢Â€ÂœWhatÃ¢Â€Â: What you are doing
(3) Ã¢Â€ÂœHowÃ¢Â€Â: How to implement it (eg programming)Ã¢Â€Â¦
...this is a Ã¢Â€Âœwhat-howÃ¢Â€Â class.
To cover the Ã¢Â€Âœwhy-whatÃ¢Â€Â I strongly recommend augmenting this class with David SilverÃ¢Â€Â™s lectures (on Youtube) and notes from a class he gave at UCL. This covers more of the theory/math behind RL but covers less on the coding. Combined together with this class it probably comprises the best RL education you can get *anywhere*, creating a 5-star combo.
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

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 Max C on 24-Oct-19

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

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 Scott L on 26-Sep-19

This course series is an incredible introduction to the basics of reinforcement learning, full stop. The course ... style, if you will, is a bit weird at first, but it seems to have been done on purpose with the aim of making the course somewhat timeless; they are presenting maths that will not change, in a format that will (hilariously) be no more slightly corny and weird in 2030 as it is in 2019.

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.

By Marius L on 20-Sep-19

Overall, I found the course well made, inspiring and balanced. The videos really helped me to understand the rather austere textbook. I give 4 stars because some of the coding exercises felt more like work in progress, without the help of other students I would not have been able to overcome these issues.

By Neil S on 12-Sep-19

This is THE course to go with Sutton & Barto's Reinforcement Learning: An Introduction. It's great to be able to repeat the examples from the book and end up writing code that outputs the same diagrams for e.g. Dyna-Q comparisons for planning. The notebooks strike a good balance between hand-holding for new topics and letting you make your own msitakes and learn from them.I would rate five stars, but decided to drop one for now as there are still some glitches in the coding of Notebook assignments, requiring work-arounds communicated in the course forums. I hope these will be worked on and the course materials polished to perfection in future.

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 David P on 3-Nov-19