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For some context or background when evaluating if this idea is good to think about:

Exploring the "Repeating Inputs" problem

I thought I would start a thread to discuss and share ideas on how to solve the “repeating inputs” problem that the temporal memory algorithm currently experiences. For those not familiar with the problem, this thread goes into it in some detail with visuals. Kicking off the discussion, I thought I would start with the idea that I suggested in the above thread: Here is a demo of the above strategy in action. In this case I simply added “max new synapse count” new synapses each timestep (up …

Suggest change to Sequence Memory algorithm

If I understand correctly, I think the official TM algorithm already does this. If a segment has “max new synapse count” active synapses when its post-synaptic cell becomes predicted active, then it will not grow any additional synapses. The relevant pseudocode from BAMI: 46. newSynapseCount = (SAMPLE_SIZE - 47. numActivePotentialSynapses(t-1, learningSegment)) 48. growSynapses(learningSegment, newSynapseCount) Here, “SAMPLE_SIZE” is the more commonly referred to “max new syn…

Repeating input as timing mechanism

(breaking this discussion off from another topic) Before I go into repeating inputs as a poor-man’s timing mechanism, let me first give a simple example of where timing is important for a game agent. Consider the case of a multi-coin block in Super Mario Brothers which is positioned two squares higher than Mario’s head (this is the environment I am using to test my own RL implementation, so it is an easy one for me to discuss). Each time Mario knocks a coin out of the block, there is a con…