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It's fair because you get better at gaming in general when you play games. For instance, your performance on Mario will be better after playing a lot of Sonic.

This is in contrast to current models which generally need to be trained up from scratch on each new game, even if the games are mechanically similar.

I think maybe a reasonable choice would be to give the ai the ability to store some limited amount of information, let's say 4kb, and it gets to populate this on a first run and draw from it on a second run. Second run scores are compared.

That's what makes this contest interesting, they're not going to just evaluate a static agent, you submit a docker environment capable of performing learning, and they'll let your agent learn on their secret levels, on their servers.

That provides a rather unique challenge, of making a good general baseline agent, but one that also is flexible for further training.

It's Sonic, clearing it is really easy for humans.