In optimizing their transportation services, Uber uses evolutionary strategies and genetic algorithms to train deep neural networks through reinforcement learning. A lot of difficult words in one sentence; you can imagine the complexity of the process.

Because it is particularly difficult to observe the underlying dynamics of this learning process in neural network optimization, Uber built VINE – a Visual Inspector for NeuroEvolution. VINE helps to discover how evolutionary strategies and genetic optimizing are performing under the hood. In a recent article, they demonstrate how VINE works on the Mujoco Humanoid Locomotion task.

[…] In the Humanoid Locomotion Task, each pseudo-offspring neural network controls the movement of a robot, and earns a score, called its fitness, based on how well it walks. [Evolutionary principles] construct the next parent by aggregating the parameters of pseudo-offspring based on these fitness scores […]. The cycle then repeats.

Uber, March 2018, link

VINE plots parent neural networks and their pseudo-offspring according to their performance. Users can then interact with these plots to:

  • visualize parents, top performance, and/or the entire pseudo-offspring cloud of any generation,
  • compare between and within generation performance,
  • and zoom in on any pseudo-offspring (points) in the plot to display performance information.

The GIFs below demonstrate what VINE is capable of displaying:

The evolution of performance over generations. The color changes in each generation. Within a generation, the color intensity of each pseudo-offspring is based on the percentile of its fitness score in that generation (aggregated into five bins). [original]
Vine allows user to deep dive into each single generation, comparing generations and each pseudo-offspring within them [original]
VINE can be found at this link. It is lightweight, portable, and implemented in Python.