signal_game

This code implements the signalling game described in [1]. The game proceeds as follows:

• Sender is shown a target image alongside with one or many distractor images,
• Sender sends a one-symbol message to Receiver,
• Receiver obtains Sender's message and all images in random order,
• Receiver predicts which of the received images is the target one and agents are rewarded if the prediction is correct.

The game can be launched with the following command (with appropriate path to the data):

python -m egg.zoo.signal_game.train --root=/private/home/kharitonov/work/egg/data/concepts/

To retrieve the data, please contact the author of the original PyTorch implementation: https://github.com/DianeBouchacourt/SignalingGame .

The game can be configured with the following command-line parameters:

• --root specifies the root folder of the data set
• --tau_gs set the softmax temperature for Sender; valid for both Reinforce and Gumbel-Softmax training (defaults to 10.0)
• --game_size set the number of the images used (one of them is the target, the rest are distractors, default: 2)
• --same whether the distractor images should be sampled from the same concept as the target (either 0 or 1, default: 0)
• --vocab_size sets the number of symbols for communication (default: 100)
• --batch_size sets the batch size (default: 32)
• --embedding_size sets the size of the symbol embeddings used by Receiver (default: 50)
• --hidden_size the hidden layer size used by both agents (default: 20)
• --batches_per_epoch how many batches per epoch (default: 100)
• --mode specifies which training mode will be used - either Gumbel Softmax relaxation (--mode=gs) or Reinforce (--mode=rf) (default: rf)
• --gs_tau sets the Gumbel Softmax relaxation temperature (default: 1.0)

It also accepts parameters that are common for all games, e.g.

• --n_epochs the number of training epochs to run (default: 10)
• --random_seed sets the random seed
• --lr , --optimizer, ... see this doc.

[1] "Multi-agent cooperation and the emergence of (natural) language, A. Lazaridou, A. Peysakhovich, M. Baroni [arxiv]