you can currently train the RL model in

• one of the [ppo,ddpg] model by --algorithm argument
• one of the ['r','rsc','rsh','rss','rsst'] mode by --env argument
  • r refers to 'rotate'
  • rsc refers to 'rotate, scale'
  • rsh refers to 'rotate, shear'
  • rss refers to 'rotate, shear, scale'
  • rsst refers to 'rotate, shear, scale, translate'

to train the model (MNIST)

$ python main.py --algorithm=ppo --gpu_number=0 --epochs=1 --save_dir=r_save --env=r

for real-world dataset,

$ python main.py --algorithm=ppo --gpu_number=0 --epochs=1 --save_dir=r_save --env=r --data_type=cifar10

after running the command,

• dataset in reference to the affined (rotate in the upper case) MNIST is generated in data directory
• all the savings(such as image or checkpoint) are saved in save/ppo/r_save directory

to continue train with the checkpoint in the r_save directory

$ python main.py --algorithm=ppo --gpu_number=0 --epochs=1 --save_dir=r_save --env=r --continue_train

to test the model

$ python main.py --algorithm=ppo --gpu_number=0 --save_dir=r_save --env=r --test