I’m surprised as well. Btw, how many layers are you using in the deeper model? And is that fully connected? I guess some deeper models with explicit feature interactions may work better in robocode scenario, given high noise. I would try things like Deep&Cross, DeepFM, etc.
I tried a few (pretty simple) variants:
- Multiplying the features by a weight matrix. One nice feature of this is that a diagonal matrix recovers standard feature weighting, so this model should be strictly better than per-feature weights.
- A one-hidden-layer feedforward network.
- Summing up the embeddings from the above two.
I totally agree that allowing multiplicative feature interactions as you suggest should work better though!
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Return to Thread:Talk:BeepBoop/Awesome enty/reply (21).
I'm using no special encoding, just normalizing the features so they are between 0 and 1. Decision-tree-like algorithms have been tried in robocode before (e.g. Wiki_Targeting/Dynamic_Segmentation), but not in conjunction with clustering/KNN as far as I know.