Recovering structured representations from raw sensations is an ability that humans readily possess and frequently use. In this work we show it is possible to guide artificial agents to produce similar representations by giving them access to the same tools that we use to recreate the world around us. In doing so they learn to produce visual programs that succinctly express the causal relationships that give rise to their observations.
Although our work only represents a small step towards flexible program synthesis, we anticipate that similar techniques may be necessary to enable artificial agents with human-like cognitive, generalisation and communication abilities.
Watch the video here, read more about the method in the paper.
This work was done by Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S. M. Ali Eslami and Oriol Vinyals, with thanks to Oleg Sushkov, David Barker, Matej Vecerik and Jon Scholz for their help with the robot.
Source: https://deepmind.com/blog/article/learning-to-generate-images
