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Brown University

Reinforcement Learning

Brown University and Georgia Institute of Technology via Udacity

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Overview

You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective. You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.



Why Take This Course?

This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.

Syllabus

  • Reinforcement Learning Basics
  • Introduction to BURLAP
  • TD Lambda
  • Convergence of Value and Policy Iteration
  • Reward Shaping
  • Exploration
  • Generalization
  • Partially Observable MDPs
  • Options
  • Topics in Game Theory
  • Further Topics in RL Models

Taught by

Charles Isbell and Michael Littman

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Reviews

2.9 rating, based on 8 Class Central reviews

Start your review of Reinforcement Learning

  • Anonymous
    Extremely slow paced, with most of the topics handwaved. I've read Sutton's book. Topics (not all) from the book were covered on a shallow level. Additional topics were explained in such a way that you need to read other resources in order to actually implement them.

    There are better books and resources on RL, don't waste time on this course
  • Jason Wu
    One of the best course. I have learned since their machine learning course and love the interaction between the lecturer. Some people complained about the slow pace, but there is actually a simplify version of RL in the ML class I've mentioned. Go…
  • Anonymous
    I think this course is awesome! The constant interaction between both professors really clarifies concepts and helps to avoid some bias about certain topics. However, as another reviewer, this course is not meant for beginners in the RL domain. I recommend taking Prof. David Silver's free online lectures before this course. I am experimenting that mixture and both courses complement superbly. This course is a more advanced and fast-paced course in RL compared to standard RL literature. At the begging it seems challenging but they encourage you to think about the problem and not just giving you all the solutions. I really recommend this course if you want to do research in RL and then Deep Reinforcement learning fields.
  • Anonymous
    I am not assessing the whole course cause I couldn't make it through the first week because I didn't like it very much. The pace is very slow and the course doesn't seem to be very thorough. There is lots of redundant chit-chat, analogies and talking that doesn't contribute to better understanding. It's also quite chaotic and unsystematic. Maybe it's a good idea for people that are not very bright and have no knowledge whatsoever on the topic, but otherwise I wouldn't recommend it. (As I mentioned my opinion is based solely on the first chapter - MDPs)
  • Profile image for SoonYau Cheong
    SoonYau Cheong
    The pace was extremely slow and there were too much nonsense going on between the two speakers. Just give the equations, explaination, examples and then move on!
  • Vinayak Mehta
    The way in which the instructors teach is awesome.

    This is a masters level machine learning course. I would recommend taking this course at a slow pace if you're a beginner in the machine learning domain, making sure that you get a thorough understanding of the material.
  • Anonymous
    Not as a first-time Reinforcement Learning course. You will have issues. Take David Silver's available on YouTube first and then come to this one. You'll enjoy it much more that way.
  • Anonymous
    Looks very slow and waste of time. Material is not matching with the lectures. Also, need to give codes for offline trials that only doing filups in lectures.
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