mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
"""
|
|
"""
|
|
import argparse
|
|
|
|
from rl_coach.base_parameters import TaskParameters
|
|
from rl_coach.coach import expand_preset
|
|
from rl_coach import core_types
|
|
from rl_coach.utils import short_dynamic_import
|
|
|
|
# Q: specify alternative distributed memory, or should this go in the preset?
|
|
# A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it.
|
|
|
|
|
|
def heatup(graph_manager):
|
|
num_steps = graph_manager.schedule_params.heatup_steps.num_steps
|
|
while len(graph_manager.agent_params.memory) < num_steps:
|
|
time.sleep(1)
|
|
|
|
|
|
def training_worker(graph_manager, checkpoint_dir):
|
|
"""
|
|
restore a checkpoint then perform rollouts using the restored model
|
|
"""
|
|
# initialize graph
|
|
task_parameters = TaskParameters()
|
|
task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir
|
|
graph_manager.create_graph(task_parameters)
|
|
|
|
# save randomly initialized graph
|
|
graph_manager.save_checkpoint()
|
|
|
|
# TODO: Q: training steps passed into graph_manager.train ignored?
|
|
# TODO: specify training steps between checkpoints (in preset?)
|
|
# TODO: replace outer training loop with something general
|
|
# TODO: low priority: move evaluate out of this process
|
|
|
|
heatup(graph_manager)
|
|
|
|
# training loop
|
|
for _ in range(10):
|
|
graph_manager.phase = core_types.RunPhase.TRAIN
|
|
graph_manager.train(core_types.TrainingSteps(1))
|
|
graph_manager.phase = core_types.RunPhase.UNDEFINED
|
|
|
|
graph_manager.evaluate(graph_manager.evaluation_steps)
|
|
|
|
graph_manager.save_checkpoint()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-p', '--preset',
|
|
help="(string) Name of a preset to run (class name from the 'presets' directory.)",
|
|
type=str,
|
|
required=True)
|
|
parser.add_argument('--checkpoint_dir',
|
|
help='(string) Path to a folder containing a checkpoint to write the model to.',
|
|
type=str,
|
|
default='/checkpoint')
|
|
args = parser.parse_args()
|
|
|
|
graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True)
|
|
|
|
training_worker(
|
|
graph_manager=graph_manager,
|
|
checkpoint_dir=args.checkpoint_dir,
|
|
)
|
|
|
|
if __name__ == '__main__':
|
|
main()
|