  Inspired by the great success of unsupervised learning in Computer Vision and
Natural Language Processing, the Reinforcement Learning community has recently
started to focus more on unsupervised discovery of skills. Most current
approaches, like DIAYN or DADS, optimize some form of mutual information
objective. We propose a different approach that uses reward functions encoded
by neural networks. These are trained iteratively to reward more complex
behavior. In high-dimensional robotic environments our approach learns a wide
range of interesting skills including front-flips for Half-Cheetah and
one-legged running for Humanoid. In the pixel-based Montezuma's Revenge
environment our method also works with minimal changes and it learns complex
skills that involve interacting with items and visiting diverse locations. The
implementation of our approach can be found in this link:
https://github.com/amujika/Open-Ended-Reinforcement-Learning-with-Neural-Reward-Functions.
