[mlpack] GSoC 2020 Proposal Discussion

[Sriram S K]

Hi everyone,

I'm going to be applying for GSoC this summer and my proposal centers
around extending the Reinforcement Learning module of mlpack.

In summary, what I propose to do is:

1) Rainbow DQN

3 of its 6 components are already implemented in mlpack, leaving Dueling,
Distributional and Noisy DQN. Since these are extensions to the standard
DQN which is already implemented, I estimate that implementing these 3
DQN's, and then Rainbow itself, along with documentation and tests should
take around 6 weeks.

2) Actor Critic Models and algorithms

If I'm not mistaken, currently mlpack does not implement Actor-Critic
models, though there is an issue open (#2262). If no one has implemented it
by then, I propose laying the foundation by implementing the basic
architecture, and if time permits, add one of the more state-of-the-art
algorithms for training it (e.g. A2C). On the other hand, if it is
implemented, I will stick to implementing extensions to the AC model (I'm
currently deliberating between A2C, Soft Actor-Critic and Optimistic
Actor-Critic) in the remaining 6 weeks.

I'd like to know if the timeline I've proposed is realistic, if all
assumptions are correct, and all algorithms mentioned are of relevance to
mlpack.

Thanks for your time!

Yours Sincerely,
Sriram S K
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