Transform your Organization with Data Science, Machine Learning and Artificial Intelligence
Deep reinforcement learning is behind the breakthroughs of Alpha Go and the research of Deepmind.
This survey gives a good summary of the field.
Note that the paper Deep Reinforcement Learning: An Overview also summarizes recent research in Deep Reinforcement Learning. It is much longer (70 pages) but maybe more complete.
This is a talk by a researcher of Open AI, Ilya Sutskever on meta-learning and self-play. Meta learning is the ability to learn to solve not only one task but also several tasks. Self-play is when a reinforcement learning algorithm plays against itself.
A great website to sort out research papers in Machine Learning is arxiv sanity. This was developed by Andrej Karpathy now at Tesla. Look at the introductory video:
You can save papers that you like and look for similar papers as ranked by their tf-idf statistics. What is missing is probably a social score of these papers such as which paper is popular at the moment though there are the top saved papers which can serve as a proxy for popularity.
A challenge of complex machine learning models is to develop trust in the models. If it is a black box some users might not be feel comfortable using them. Models need to be interpretable, meaning that users should be able to understand how the outputs (predictions) are generated from the inputs (features).
Different approaches have been suggested. A recent one is a technique called Local Interpretable Model-agnostic Explanations (LIME). LIME approximates a model with an interpretable model locally. An interpretable model is a model such as linear models with a limited number of features.
A short video introduces the approach.
You can read the paper here.