def main(args):
training.utility.set_up_logging()
experiment = training.Experiment(args.logdir,
process_fn=functools.partial(process,
args=args),
num_runs=args.num_runs,
ping_every=args.ping_every,
resume_runs=args.resume_runs)
for run in experiment:
for unused_score in run:
pass
def main(args):
training.utility.set_up_logging()
experiment = training.Experiment(args.logdir,
process_fn=functools.partial(process,
args=args),
start_fn=functools.partial(start,
args=args),
resume_fn=functools.partial(resume,
args=args),
num_runs=args.num_runs,
ping_every=args.ping_every,
resume_runs=args.resume_runs)
for run in experiment: # the method __iter__(self) defined in experiment.
for unused_score in run: # the method __iter__(self) defined in run.
pass
def trainable(config):
print('begin a trial')
args.params = tools.AttrDict(yaml.safe_load(args.params.replace('#', ',')))
args.logdir = args.logdir and os.path.expanduser(args.logdir)
print('debug ', config["divergence_scale"], config["reward_loss_scale"])
with args.params.unlocked:
args.params.divergence_scale = config["divergence_scale"]
args.params.reward_loss_scale = config["reward_loss_scale"]
# args.params.main_learning_rate = config["main_learning_rate"]
args.params.test_steps = 50
# args.params.num_units = config['num_units']
args.params.test_traj = 5
training.utility.set_up_logging()
experiment = training.Experiment(
args.logdir,
process_fn=functools.partial(process, args=args),
num_runs=args.num_runs,
ping_every=args.ping_every,
resume_runs=args.resume_runs)
for run in experiment:
for test_score in run:
if test_score > 1.0:
tune.report(mean_score=test_score)
break
def main(args):
import dowel
from dowel import logger, tabular
training.utility.set_up_logging()
stages = {'500k': 'model.ckpt-2502500'}
# stages = {'1000k': 'model.ckpt-5005000'}
num_traj = 10
# stages = {'100k': 'model.ckpt-500500', '500k': 'model.ckpt-2502500'}
# stages = {'1M': 'model.ckpt-5005000'}
# stages = {'final': 'model.ckpt-2652650'}
# stages = {'100k': 'model.ckpt-500500', '500k': 'model.ckpt-2502500', '1M': 'model.ckpt-5005000'}
# stages = {'100k': 'model.ckpt-600500', '500k': 'model.ckpt-3002500',
# 'final':'model.ckpt-3182650'}
# methods = ['weighted_100']
# methods = ['aug7']
methods = ['baseline3']
# rival_method = 'baseline3'
rival_method = 'aug7'
rival_runs = 5
base_dir = 'benchmark'
envs = ['finger_spin']
# envs = ['cartpole_swingup']
# envs = ['finger_spin', 'cartpole_swingup','cheetah_run', 'cup_catch']
# envs = ['finger_spin', 'cartpole_swingup', 'reacher_easy', 'cheetah_run']
# envs = ['cartpole_swingup', 'cheetah_run', 'walker_walk', 'cup_catch']
# envs = ['finger_spin', 'cartpole_swingup', 'reacher_easy', 'cheetah_run', 'walker_walk', 'cup_catch']
if not check_finish(base_dir, stages, methods, envs, args.num_runs):
exit()
for pref, chkpt in stages.items():
print(pref, 'begin')
logger.add_output(dowel.StdOutput())
logger.add_output(dowel.CsvOutput('benchmark_{}.csv'.format(pref)))
for env in envs:
tabular.record('Env', env)
for method in methods:
for id in range(rival_runs):
means, stds, all_scores = [], [], []
with args.params.unlocked:
args.params.chkpt = chkpt
args.params.tasks = [env]
args.params.planner_horizon = 12
args.params.eval_ratio = 1 / num_traj
# args.params.r_loss = 'contra'
# args.params.aug = 'rad'
args.params.planner = 'dual2'
args.params.rival = '{}/{}/00{}'.format(
env, rival_method, id + 1)
experiment = training.Experiment(
os.path.join(base_dir, env, method),
process_fn=functools.partial(process, args=args),
num_runs=args.num_runs,
ping_every=args.ping_every,
resume_runs=args.resume_runs,
planner=args.params.planner,
task_str=env)
for i, run in enumerate(experiment):
scores = []
for i, unused_score in enumerate(run):
print('unused', unused_score)
scores.append(unused_score)
if i == num_traj - 1:
break
means.append(np.mean(scores))
stds.append(np.std(scores))
all_scores.append(scores)
print(means)
# if args.params.planner != 'cem':
# exit()
if args.params.planner == 'cem_eval':
np.save(
os.path.join(
args.logdir, env, method,
'00{}/scores_{}_cem.npy'.format(i, pref)),
np.array(all_scores))
mean, std = np.mean(means), np.std(means)
print('{} {}+/-{}'.format(method, int(mean), int(std)))
if mean > 0:
tabular.record(method,
'{}+/-{}'.format(int(mean), int(std)))
np.save(
os.path.join(args.logdir, env, method,
'scores_{}.npy'.format(pref)),
np.array(all_scores))
logger.log(tabular)
logger.dump_all()
logger.remove_all()