[mlpack] GSoc '18 Project Idea 'Reinforcement Learning'

[Rishi Kaushik]

Hello everyone,
My name is Rishi Kaushik and I am a Computer Science major at BITS,
Hyderabad, India. I am a Machine Learning enthusiast and am well versed
with C/C++, Java and Python. I have done a few basic projects implementing
ML algorithms in C++ and Java and the list of projects mlpack has offered
really excites me.
I am particularly interested in the idea of the project 'Reinforcement
Learning'. What I wanted to ask was, can this idea of training a neural
network to play games be extended to more than Reinforcement Learning
algorithms? For the past few weeks I have been looking into the NEAT
(NeuroEvolution of Augmenting Topologies) genetic algorithm and have seen
implementations using NEAT playing classic games. I must admit I have no
knowledge about the efficiency of NEAT vs. RL for this specific project but
neuro evolution techniques have been shown to outperform RL methods on
benchmark tasks.
Also as the description says we can choose one or two different algorithms
I think it'll be interesting to implement a RL based technique along with
one NEAT based implementation and compare the benchmark results.
I am very much interested to learn more about this and build this project
with you, please let me know how I should proceed and if the variation I
have offered seems plausible or not.
Thank You.
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