This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.

To change hyperparameters, check out params.py. Specifically, params['sim_env'] controls whether we are using the toy environment (with hand-crafted rewards) or the ergodic search environment.

To train the allocation generator and discriminator with the pre-trained reward network weight (as a surrogate approximation to speed up training), run

python train.py

To test the allocation generator, relocate trained weights as logs/test_weights/generator_weight, and run

python test_alloc.py

(Optional) To retrain the reward network weight, run:

python train_simulation_reward.py

Put the trained weight in logs/reward_logs/reward_weight for training.

The training data for the reward network is stored in logs/training_data/*.npy