* `--keep-checkpoints=<n>` – Specify the maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the `save-freq` option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. Defaults to 5.
* `--lesson=<n>` – Specify which lesson to start with when performing curriculum training. Defaults to 0.
* `--load` – If set, the training code loads an already trained model to initialize the neural network before training. The learning code looks for the model in `python/models/<run-id>/` (which is also where it saves models at the end of training). When not set (the default), the neural network weights are randomly initialized and an existing model is not loaded.
* `--num-runs=<n>` - Sets the number of concurrent training sessions to perform. Default is set to 1. Set to higher values when benchmarking performance and multiple training sessions is desired.
* `--num-runs=<n>` - Sets the number of concurrent training sessions to perform. Default is set to 1. Set to higher values when benchmarking performance and multiple training sessions is desired. Training sessions are independent, and do not improve learning performance.
* `--run-id=<path>` – Specifies an identifier for each training run. This identifier is used to name the subdirectories in which the trained model and summary statistics are saved as well as the saved model itself. The default id is "ppo". If you use TensorBoard to view the training statistics, always set a unique run-id for each training run. (The statistics for all runs with the same id are combined as if they were produced by a the same session.)
* `--save-freq=<n>` Specifies how often (in steps) to save the model during training. Defaults to 50000.
* `--seed=<n>` – Specifies a number to use as a seed for the random number generator used by the training code.
Arthur Juliani