Google to Open Dopamine Source Code

The tool for neural network training is based on TensorFlow, a library for machine learning
30 August 2018   776

Google Brain Team published the source code of the Dopamine framework, which allows the implementation of training with reinforcement for neural networks. The repository contains 15 Python files with documentation. The tool is based on TensorFlow, a library for machine learning.

The framework is based on the Arcade Learning Environment platform, which evaluates the performance of AI using video games. Developers also got access to sets of source data for training and tests on 60 games supported by the platform. This approach makes it possible to standardize the process of working with neural networks and to obtain reproducible results.

Dopamine supports 4 learning models: deep Q-learning, C51, Implicit Quantile Network and a simplified version of Rainbow.


Simultaneously with the placement of the source code, Google launched a website with tools to visualize the process of interacting with AI via Dopamine. The site supports work with multiple agents simultaneously, provides access to statistics, training models and planning through TensorBoard.

Pablo Samuel Castro and Marc G. Bellemare, Google Brain Team researchers expressed the hope that the flexibility and ease of use of the tool developed by their group will inspire developers to try out new ideas.

This is not Google's first step towards increasing the availability of tools for neural networks. In 2017, the company announced the launch of the project, a project to democratize the achievements in the field of machine learning.

Get more info at GitHub.

Neural Network Now Can Animate People on Photos

Algorithm can even make people on the photos to 'go out' picture's borders
12 December 2018   103

Researchers at the University of Washington, together with the developers of Facebook, have created an algorithm that “revives” people in the photographs. In a single snapshot, it generates a three-dimensional moving model of a figure that can sit, jump, run, and even "go" beyond the limits of the image. The algorithm also works for drawings and anime characters.

To create such a technology, researchers used the experience of colleagues.

  • Mask R-CNN recognizes a human figure in the image and makes it stand out from the background.
  • Another algorithm imposes a simplified skeleton markup on the shape, defining how it will move.
  • The third algorithm "fills" the background space, previously hidden by the figure.

Further, the own algorithm of researchers on the basis of a marked two-dimensional figure creates a three-dimensional model and generates a texture level from the original image.

The developers added a user interface that allows you to change the shape of the figure in order to edit the photo itself or determine where the animation will begin. In addition, you can “revive” a drawing or photo in augmented reality and see a three-dimensional figure in VR or AR glasses.