simple_autoenc

simple_autoenc is a simple 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.

The communication is performed by mean of variable-length messages; the training is done by Reinforce.

The game can be run as follows:

python -m egg.zoo.simple_autoenc.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 types of the cells that are 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; those are 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)