Transformers as dynamics models
Predictive dynamics models often have excellent single-step error, but poor long-horizon accuracy due to compounding errors. We show that Transformers are more reliable long-horizon predictors than state-of-the-art single-step models, even in continuous Markovian domains.
Attention patterns of the Trajectory Transformer, showing (left) a discovered
Markovian stratetgy and (right) an approach with action smoothing.
from Hacker News https://ift.tt/oPTmBVf
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