hi shankar,

that sounds interesting to me. Can you come up with a few references
and are you aware of existing implementations?

Alex

On Wed, Mar 14, 2012 at 6:18 PM, Shankar Satish <[email protected]> wrote:
> Thank you so much for your valuable inputs, Alejandro, Paolo, and Olivier.
> Based on your feedback, i have decided to drop the idea of RL-learning for
> sklearn.
>
> Instead, here is an alternate proposal: Supervised learning with Bayesian
> Networks.
>
> Bayesian networks provide highly interpretable models, and have been used
> with notable success in medical diagnosis. If you know the graph topology
> (structure) of the bayes-net, inference operations reduce to
> maximum-likelihood/expectation maximization. However in practice, the
> graph-structure is often unknown.
>
> I propose implementing a bayes-net learner that can learn both the structure
> as well as parameters of a bayesian network from a given dataset, and
> demonstrate it's uses for prediction / inference tasks.
>
> So, what do you guys think about my new proposal? :)
>
> regards
> shankar.
>
>
>
>
>
>
> On Wed, Mar 14, 2012 at 10:22 AM, Olivier Grisel <[email protected]>
> wrote:
>>
>> 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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Virtualization & Cloud Management Using Capacity Planning
Cloud computing makes use of virtualization - but cloud computing 
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