Le 13 mars 2012 07:53, Alejandro Weinstein
<[email protected]> a écrit :
> On Tue, Mar 13, 2012 at 6:37 AM, Shankar Satish <[email protected]> wrote:
>> Do you think my proposal about implementing reinforcement-learning
>> algorithms (subject line: "GSOC project idea: online learning algorithms")
>> is something that is well suited for integration into scikit-learn? Do you
>> think it makes more sense to start a new scikit focussed on reinforcement
>> learning?
>
> Just a couple of comments. There are some RL Python implementations,
> e.g. PyBrain (http://pybrain.org/) and RL-Glue/RL-Library
> (http://glue.rl-community.org/wiki/Main_Page). It seems that none of
> these are being actively developed.
>
> The nature of RL problems implies that the architecture of the code is
> different than the "single script" approach used in scikit-learn. For
> instance, in RL-Glue/RL-Library you run three independent programs
> (the agent, environment and experiment programs) plus the RL-Glue
> process. This approach is natural because it mimics the actual RL
> problem, where the agent and the environment are two different
> entities. Also, in the case of RL-Glue, you can combine environments
> and agents written in different languages. I wonder how this different
> architecture of RL would match with the scikit-learn ecosystem.

I globally agree with that view: RL does not really fit in the current
sklearn API. Modeling agents / environment interactions currently
looks out of the scope of the project. PyBrain is probably a better
project for this kind of models. Maybe they will take part in this
year GSoC too.

I must admit I haven't thought through the problem too much though as
I don't know the RL literature enough to make an informed judgment.

-- 
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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