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License: MIT License
Experiments on Emergent Communication Through Negotiation
License: MIT License
one possible way to increase speed is by creating a dataset with utilities, item pools, and termination lengths (N) then load it with torch.data
would also be good for direct reproducibility
currently agents do masking communication in a multi-step process but this isn't fully right given that, in the real world, it doesn't make sense to later mask something you've previously unmasked.
perhaps masking communication should be additive (i.e. you should only be able to unmask items you previously masked)
I think you can greatly improve speed here
matches_argmax
over time is just measuring the highest probability in a roundabout way, a better measure of the distribution is the entropy
as per https://arxiv.org/pdf/1706.05125.pdf:
replace type_constr
with to(device)
remove Variable
for requires_grad=True
change Tensor(...)._fill(0)
to torch.zeros
change dataset generation to try and take advantage of caching to speed things up
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