[isseu]

I feel problem definition is underappreciated in current ml world and the effort is put into the ml methods. People has been able to tackle almost any problem well defined but these are usually simple in comparison to true ai so we still get "stupid" agents. What problem is the test to true ai?

[yodon]

This paper would seem to be a fairly broad based and complex set of capabilities to learn and accomplish, including both collaboration and exceeding the performance of high skill humans at a highly skill based activity. Is there something you feel is missing from the task in this paper?

[avsteele]

How much would the agent's skill level decline, and how much training would be required to recover if, for example, the game were reskinned? How does the skill in this one game mode translate into others?

Stating the critique more directly: in what way does the expertise demonstrated mirror the kind of expertise possessed by a similar level human ELO player?

If would seem a really shallow kind of expertise, if it didn't translate into some competence in very similar games.

[yodon]

The article discusses a wide range of what constitute significant conceptual mappings discovered autonomously. This work was trained on a single game with a single art direction. The AI can generalize to new maps it has not seen before within that style of art direction, including reliably beating expert human players on maps that are new to both human and AI. That is a significant generalization. The technique should be generalizable to other styles of skinning or different art direction in other games, but there is no way for the the AI to have learned that yet in this experiment because the AI was only ever exposed to a single game here. Over time this technique undoubtedly will be extended to multiple games with different styles of art direction, the challenge there being less on the AI/machine learning side and more on the effort required to hook up multiple games for use in this type of training environment.