def main(collect):
collect = misc.update_arguments(collect=collect)[0]
dataset_dir, env_name, episodes = collect_dataset(collect)
print(
colored('Dataset successfully written to {}'.format(dataset_dir),
'green'))
print(
colored('{} episodes collected from {}'.format(episodes, env_name),
'green'))
def main(model, dataset):
model, dataset = misc.update_arguments(model=model, dataset=dataset)
train_loader, eval_loader, env_name, statistics = utils.make_loader(
model=model, dataset=dataset)
net, optimizer, starting_epoch, net_path = utils.make_net(
model=model, dataset=dataset, env_name=env_name, statistics=statistics)
utils.write_info(model, dataset)
log.init_writers(net_path)
train(train_loader, eval_loader, net_path, net, optimizer, starting_epoch)
def main(model, collect):
t0 = time.time()
print('Collecting demos in folder {} with parameters:\n collect {}\n model {}'.format(
collect['folder'], collect, model))
model, collect = misc.update_arguments(model=model, collect=collect)
collector, seed_epoch, report, report_path, data_dir = make_collector(model, collect)
results = run_parallel(collector, seed_epoch, collect)
episodes, failed_episodes, tot_steps, max_steps = process_results(
results, report, report_path, collect)
print(colored('Dataset successfully written to {}'.format(data_dir), 'green'))
print(colored('Total {} steps in {} trajectories'.format(tot_steps, episodes), 'green'))
print(colored('Maximum {} steps in one trajectory'.format(max_steps), 'green'))
print(colored('Data collection took {} seconds'.format(time.time() - t0), 'green'))
# if len(failed_episodes):
# print(colored('Failed {} trajectories: {}'.format(
# len(failed_episodes), sorted(failed_episodes)), 'red'))
if report is not None:
print_report(report)