By definittion you need to pass to an Encoder data that has SIMILARITY encoded then then the data has to go trough SPooler, which have to preserve this similarity.
The question is what can you do in case where the SIMILARITY/overlap is dependent on the interaction i.e. it is known after the fact.
For example in chess you may have a State’s (S1 and S2, encoded as SDR) where a board/physical similarity can be just one figure at differrent position i.e. they are spatially similar, but another State S3 is semantically closer.
In this case action A will be more appropriate to S1 and S3, but not for S2.
If you figure out a way to encode S1 and S3 with higher overlap then the Agent will learn better.
Another example Tic-Tac-Toe, states :
are semantically equivalent … the next Action (?,1,3) wins the game. Phisycally the States are not that similar.
So I’m looking for Encoder that generates similar SDR’s with feedback from outside ? How would you approach problem like this ?