Reinforcement Learning Study Group

(Lankinen) #21

That would be great competition but I don’t have enough time and talents. If you are going to participate it would be interesting to hear your solutions.


(Rohit ) #22

I too have just started my journey in DRL not that too long ago, I felt participating in contest would provide the motivation to pick up the necessary skills at a faster rate.
If anyone is planning to start any new project, I would be interested in joining in.

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(Lankinen) #23

We are going to make some example codes to Github and this is our group where anyone can join.

In case you are interested it would be great if you can check is there mistakes in the codes. Currently the repository is empty but we will add there something soon.

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(Rohit ) #24

Cool will do so


(Rohit ) #25

This is another nice resource that I found online, the algorithms are implemented using Pytorch.

How do I add it to the list of resources available at the start of this post

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(Lankinen) #26

@ingbiodanielh can you do it?

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(Lankinen) #27

Hello everyone! We started this group a month ago and now we have multiple active members. There is still time to join and I wrote this message because of that. We had our first meeting this Monday where we talked about the first chapter of Sutton’s and Barto’s book. It wasn’t that important because the first chapter is more or less introduction to the book.

We will have another meetup week after the next and topic will be chapter 2 (multi armed bandit problem). We will have at least three amazing presentations about the topic. Now it is the best time to join our Slack, download the free RL book, and start reading it. You don’t have to talk anything in the meetings so if you just want to hear the presentations that is ok for us. In case you want to dig deeper, you can reserve some topic and have a presentation about it.

Reinforcement learning is a very important topic to understand no matter what kind of machine learning stuff you are doing. It is good to have a basic understanding because some new techniques might use RL for example loss function calculation. It can be hard to understand what the model is doing if you don’t understand how the loss function is working because it is using some simple RL technique. I would call this fastai part 3 although you don’t need to have information from part 2 to understand this.


(Less ) #28

This paper on mcp (multiplicative control) looks like a big advance for drl and robotics.

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