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Gabriel (as well as the others on the team) have definitely looked at these areas - if things were left out/not "featurized" it was likely done via an ablation test, or showed improvement over benchmarks, or maybe just to set a baseline, as he is quoted in the main article. I don't know what techniques they used here, but I am excited to find out!

On the specific issue of encoding time-dependent behaviors in models, I think it is related to a broader issue that shows up in many application areas. To me the critical factor is that these models are ruthlessly good at exploiting local dependencies and totally forgetting long-term global dependencies or respecting required structure in control/generation.

This basically means it is very difficult to train long-term, time dependent behavior without tricks (early/mid/late game models, extensive handcrafting of the inputs, or using high level "macro actions"). Indeed, FAIR's recent mini-RTS engine ELF directly gives macro actions, in part to look closer at how well global strategies are really handled and remove one factor of complexity [0].

Gabriel's PhD thesis was entirely on Bayesian models for RTS AI, applied to SC:BW [1], so I am sure he is well aware of the "classic/rules based" approaches for this.

[0] https://code.facebook.com/posts/132985767285406/introducing-...

[1] http://emotion.inrialpes.fr/people/synnaeve/phdthesis/phdthe...



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