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View Code? Open in Web Editor NEWAn implementation of Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch and Pieter Abbeel
An implementation of Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch and Pieter Abbeel
in the modules/word_counting.py
it seems like the cost returned is not as described in paper
# uterances is a tensor of probabilities of dim: batch_size, num_agents, vocab_size
# world_counts is a tensor of dimenstion: vocab_size
cost = -(utterances/(self.oov_prob + self.word_counts.sum() - 1)).sum()
notice how self.word_counts.sum()
is a scalar
thus self.oov_prob + self.word_counts.sum() - 1
is a scalar as well and could be factored out
thus cost = - utterances.sum() / some scalar
since uterances
is a tensor of probabilities utterances.sum()
is a constant.
Thus cost is a harmonic series "1, 1/2, 1/3, 1/4, ...", independent of the actual utterances of the agents.
This doesn't seem to follow the penatly of the Dirichlet Process.
Hi,
I did not find the model.py, could you please upload that?
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