Hello everyone,
I am Yaowen, a fourth year undergraduate student studying Electrical Engineering at National Taiwan University. Programming is my interest. I like to investigate frameworks and features of programming languages, and create some fun projects with them. Currently I am doing research in the area of Deep Reinforcement Learning. When I am looking for a cool project for GSoC 2017, the "Reinforcement Learning" project on the mlpack github wiki really caught my attention. Actually, I read lots of recent papers about reinforcement learning, and work on models like DQN, A3C with my partners in the lab using tensorflow. So I think this project is really cool and just meets what I am working on. I wonder that if mlpack can get a better performance compared to tensorflow and other frameworks on a multi-core CPU. Now I am diving into the mlpack codebase and have a question. Recently, lots of works in papers have complex models which consist multiple inputs and outputs. And they may have multiple optimization stages when training them. In each stage, the algorithm trains a different set of parameters and some parameters are regarded as constants. In the meanwhile, some layers in the models could be recurrent neural networks, which need to reset the state from time to time. Since I am not familiar with the codebase and all API related to ann, I am curious about how to handle all of these? Currently I only found that one can use Forward and Backward to handle complex training schemes manually. But as for high-level APIs, I guess FNN and RNN are not capable to handle these things? BTW, as already mentioned by others, adding the ELU activation function is really a good idea. :) Thanks, Yaowen
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