About
The task is to train an agent to play SNES games such as Super Mario.
( Image credit: Large-Scale Study of Curiosity-Driven Learning )
Benchmarks
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Greatest papers with code
Large-Scale Study of Curiosity-Driven Learning
ICLR 2019
• openai/large-scale-curiosity
• 
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.
Ranked #9 on
Atari Games
on Atari 2600 Freeway
Playing SNES in the Retro Learning Environment
• nadavbh12/Retro-Learning-Environment
• 
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.
Ranked #1 on
SNES Games
on F-Zero
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Being able to reach any desired location in the environment can be a valuable asset for an agent.