README.md

channel is a Sender/Receiver game where a pair of agents is trained to encode and decode (i.e. autoencode) a one-hot vector of a fixed dimension and transmit it over channels with different properties. The vectors to be auto-encoded might come from uniform, powerlaw or some specified distribution. The communication is performed by mean of variable-length messages; the training is done by Reinforce/GS.

This code was used in the experiments of the following paper:

• Anti-efficient encoding in emergent communication. Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni. NeurIPS 2019. arxiv

The game can be run as follows:

python -m egg.zoo.channel.train --vocab_size=3 --n_features=6 --n_epoch=50 --max_len=10 --batch_size=512 --random_seed=21

The game accepts the following game-specific parameters:

• max_len -- the maximal length of the message. Receiver's output is checked either after <eos> symbol is received or after max_len symbols;
• vocab_size -- the number of unique symbols in the vocabulary (inluding <eos>!)
• sender_cell/receiver_cell -- the cells used by the agents; can be any of {rnn, gru, lstm}
• n_features -- the dimensionality of the vectors that are auto-encoded
• n_hidden -- the size of the hidden space for the RNN cells
• embed_dim -- the size of the hidden space for the RNN cells
• sender_entropy_coeff/receiver_entropy_coeff -- the regularisation coefficients for the entropy term in the loss, used to encourage exploration in Reinforce
• sender_hidden/receiver_hidden -- the size of the hidden layers for the cells
• sender_lr/receiver_lr -- the learning rates for the agents' parameters (it might be useful to have Sender's learning rate lower, as Receiver has to adjust to the changes in Sender)
• probs={p1,p2,...} or 'probs=powerlaw' select the prior distribution over concepts

Reproducibility

If you want to recover results maximally close to those reported in the paper, please use EGG v1.0. This can be done by running the following command:

git checkout v1.0

In later versions of EGG, some metrics are aggregated differently, which might lead to small discrepancies.