The task is to train an agent to play SNES games such as Super Mario.
However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent.
7 Nov 2016
The environment is expandable, allowing for more video games and consoles to be easily added to the environment, while maintaining the same interface as ALE.
2 May 2018
This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.
We show how this network can be efficiently trained with a 3D variant of Q-learning to update the estimates towards all goals at once.