
Deep Reinforcement Learning Explained - ktRolster
http://www.nervanasys.com/demystifying-deep-reinforcement-learning/
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skewart
This is one of the best explanations of an ML topic that I've read in a long
time. It strikes a great balance of being approachable for non-experts and
being in-depth enough to give a reader a feeling that they understand how
things work.

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ktRolster
A lot of machine learning documentation/explanations are really difficult, the
math is really deep.

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sjg007
There's the grad student from STanford who describes ML and NNs using circuits
and a"forces" analogy which may be useful for CS folks. I found it
illuminating.

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raverbashing
[https://karpathy.github.io/neuralnets/](https://karpathy.github.io/neuralnets/)

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xiphias
It's not unexpected, but still strange that the patent wars have started in
the way to AGI. It puts together ideas that were done by researchers and seems
to be something that will be crutial for robotics in the future.

~~~
xiphias
Also I always thought that smart kitchen robots that can peel potatoes for me
and do a limited set of things will come to my home, but from these algorithms
it seems that the hardware is behind the software, so even the first good
kitchen robots will be general enough to learn any motion (but not precise
enough for everything that a human can do)

~~~
ktRolster
How large do you want your training set to be to get it to peel potatoes
properly? :)

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xiphias
The algorithm should be good enough to learn from the current youtube videos
:)

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T-A
Another one, with Python code to play with: [http://outlace.com/Reinforcement-
Learning-Part-3/](http://outlace.com/Reinforcement-Learning-Part-3/)

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ankurdhama
I have one question. Did they trained different network to play different game
i.e for each game they have corresponding trained network OR were they able to
train a single network to play 2 or more games?

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matt4077
The first paragraph has an answer: they trained individual networks sharing
structure but not data.

