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I am doing actor-critic reinforcement learning for an environment that is best represented as a "bag-of-words". For this reason, I have opted to use a single body, multi-head approach for the net architecture. I use a linear pre-processing layer to generate n word embeddings of dimension d. Then I run the (batch,n,d) words through a stack of (2) nn.TransformerEncoder layers for the body and each head is another encoder layer followed by a linear logit layer.

Since this is RL and I have limited compute, it is also difficult to evaluate training as its happening. I decided to try looking a the mean cosine similarity of the latent words after the encoder body. My intuition tells me if the net is learning a proper latent representation of the environment then dis-similar words should have low cosine similarity.

However even though the net is clearly improving somewhat the mean cosine sim. remains very high, > .99

Thinking about it more, I don't think there's any reason to believe my first intuition, especially since I am not even normalizing the words after encoder body stack. But even if I did normalize, I'm not sure that would encourage lower cosine sim. as I am using 256 dimensions per word. All normalizing does is reduce the dimension of the output space by 1, which should hardly matter here.

Does this make sense? Also any general advice about my net is welcome

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