Usage¶
To start training:
$ python train.py --sys ... --hparams ... --output_dir ...
Run tensorboard to visualize training:
$ tensorboard --logdir ...
Test agent:
$ python train.py --sys ... --hparams ... --output_dir ... --training False --render True
The sys command can be one of two options: local
or tpu
for GCP enabled tpu training. A list of environments and hyperparameters can be found under rl/rl/hparams
. A full training and evaluation example can be found in the tutorial section.
Below we summerize the key arguments
“--sys”(str) defines the system chosen to run experiment with; e.g. “local” for running on the local machine.
“--env”(str) specifies the environment.
“--hparam_override”(str) overrides hyperparameters.
“--train_steps”(int) sets training length.
“--test_episodes”(int) tests episodes.
“--eval_episodes”(int) sets Validation episodes.
“--training"(bool) freeze model weights is set to False.
“--copies”(int) set the number of times to perform multiple versions of training/ testing.
“--render”(bool) turns rendering on/ off.
“--record_video”(bool) records the video with, which outputs a .mp4 of each recorded episode.
“--num_workers"(int) seamlessly brings our synchronous agent into an asynchronous agent.