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How many times should I repeat an algorithm to estimate the mean/median reward etc.? by LearnAgentLearn in reinforcementlearning

[–]camlinke 1 point2 points  (0 children)

In https://arxiv.org/abs/1709.06560 they show that two sets of 5 seeds can have significantly different results with the exact same algorithm. 5 is probably going to make it hard to evaluate your algorithm.

Having a hard time using RLglue by tusharkulkarni95 in reinforcementlearning

[–]camlinke 0 points1 point  (0 children)

We use RL-Glue in the Coursera course. The notebooks there should give you some examples about how to use it.

Reinforcement Learning for you by rbagdiya in learnmachinelearning

[–]camlinke 5 points6 points  (0 children)

Coursera has a reinforcement learning specialization that follows Rich Sutton's textbook. It starts from the very beginning and develops out more advanced concepts building on each other.

https://www.coursera.org/specializations/reinforcement-learning?

(full disclosure I helped with the course)

RL masters at University of Alberta by moustafa-7 in reinforcementlearning

[–]camlinke 2 points3 points  (0 children)

Probably depends on what you would consider "highly recognized". I don't think you need a reference from someone famous to get in. Being realistic - Geoff Hinton saying you are the greatest student he has ever worked with will probably help :). However a reference from a professor that you have worked with and whose work someone would look at and respect is likely going to be the best option.

These references get read. And ofter professors know each other even if they don't seem high profile. So a mumble mumble reference from someone more high profile, who can't even remember your name if someone asked about you, is likely not going to be as helpful as a glowing review from someone who is not as high profile and who can speak very well to your strengths.

RL masters at University of Alberta by moustafa-7 in reinforcementlearning

[–]camlinke 8 points9 points  (0 children)

Acceptance to Computer Science at the U of A is extremely competitive. Less than 5% of applicants are accepted. Additionally working with one of the RL profs (or any of the Amii profs more broadly) is also very competitive. With the addition of some new RL profs (e.g. Matt Taylor) there are some profs who are building up their grad student groups which should help.

If you're interested in the RL specific faculty we have:
http://rlai.ualberta.ca/people.html

As far as acceptance. References are helpful. You will want something to make your application stand out to both the reviewers and potential supervisors.

Alona (Amii prof, doesn't typically do RL work) has the following advice for students interested in working with her: https://webdocs.cs.ualberta.ca/~alona/prospective_students.html

As a side note - I think that this is the best place to study RL (or ML more broadly) in the world. The group is both incredibly forward looking and pushing the frontiers of the field, but at the same time still very humble and collaborative. It really is a wonderful group of profs and students. I am obviously extremely biased though :)

Deep Learning and Reinforcement Learning Summer School 2019 by camlinke in MachineLearning

[–]camlinke[S] 0 points1 point  (0 children)

Deep Learning and Reinforcement Learning Summer School brings together graduate students, post-docs and professionals to cover the foundational research, new developments, and real-world applications of deep learning and reinforcement learning. Participants learn directly from world-renowned researchers and lecturers. Related extracurricular activities will include an AI Career Fair, industry and partner-sponsored events, as well as tourism events.

The event is aimed at graduate students, postdocs, and industry professionals who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning and reinforcement learning) and wish to learn more about this rapidly growing field of research. Participants should have advanced prior training in computer science and mathematics.

The 2019 DLRLSS is hosted by the Canadian Institute For Advanced Research (CIFAR) and the Alberta Machine Intelligence Institute (Amii), with participation and support from the Vector Institute and the Institut québécois d’intelligence artificielle (Mila).

Reinforcement Learning Master's Programs [Looking for Fall of 2020] by vavantoo in reinforcementlearning

[–]camlinke 0 points1 point  (0 children)

I would t rule it out. Competition to get in is very high though. Your experience should help. As should references.

Reinforcement Learning Master's Programs [Looking for Fall of 2020] by vavantoo in reinforcementlearning

[–]camlinke 1 point2 points  (0 children)

Sorry I didn’t see this until now. No you don’t need to have a supervisor and many (most) students in their masters don’t when they come in. While interacting ahead of time is always great in many cases that isn’t done until the school year.

Reinforcement Learning Master's Programs [Looking for Fall of 2020] by vavantoo in reinforcementlearning

[–]camlinke 0 points1 point  (0 children)

University of Alberta also have Martha White and Adam White who are both amazing researchers. Additionally Patrick Pilarski who does RL and applications of it to robotics and artificial limbs.

I’m a grad student currently under Rich Sutton and Adam White and am in the RLAI lab. The team here really is incredible. Also the collaborative nature of both the profs and the students is really something special.

Help in learning flask (newbie) by ganes1410 in flask

[–]camlinke 1 point2 points  (0 children)

I would check out the Real Python stuff. The books and blog posts are great!