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Brown University

Reinforcement Learning

Brown University and Georgia Institute of Technology via Udacity

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Overview

Study machine learning at a deeper level and become a participant in the reinforcement learning research community.

Syllabus

  • Introduction to Deep Reinforcement Learning
  • Value-Based Methods
    • Combine cutting-edge reinforcement learning techniques with deep learning architectures. Use Q-networks and experience replay to train agents capable of navigating and adapting to real-time environments.
  • Policy-Based Methods
  • Multi-Agent Reinforcement Learning
  • Special Topics in Deep Reinforcement Learning
  • Neural Networks in PyTorch
  • Computing Resources
  • C++ Programming

Taught by

Charles Isbell and Michael Littman

Tags

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
    4
    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…
  • Vinayak Mehta
    27
    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
    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.
  • Profile image for SoonYau Cheong
    SoonYau Cheong
    21
    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!
  • 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)
  • 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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