def test_no_crash_cartpole(self):
config = self._define_config()
with config.unlocked:
config.env = 'CartPole-v1'
config.max_length = 200
config.steps = 500
config.normalize_ranges = False # The env reports wrong ranges.
config.network = networks.feed_forward_categorical
for score in train.train(config, env_processes=True):
float(score)
def test_no_crash_cartpole(self):
config = self._define_config()
with config.unlocked:
config.env = 'CartPole-v1'
config.max_length = 200
config.steps = 500
config.normalize_ranges = False # The env reports wrong ranges.
config.network = networks.feed_forward_categorical
for score in train.train(config, env_processes=True):
float(score)
def test_pendulum_no_crash(self):
nets = networks.feed_forward_gaussian, networks.recurrent_gaussian
for network in nets:
config = self._define_config()
with config.unlocked:
config.env = 'Pendulum-v0'
config.max_length = 200
config.steps = 500
config.network = network
for score in train.train(config, env_processes=True):
float(score)
def test_no_crash_variable_duration(self):
config = self._define_config()
with config.unlocked:
config.env = functools.partial(
tools.MockEnvironment, observ_shape=(2, 3), action_shape=(3,),
min_duration=5, max_duration=25)
config.max_length = 25
config.steps = 100
config.network = networks.recurrent_gaussian
for score in train.train(config, env_processes=False):
float(score)
def test_pendulum_no_crash(self):
nets = networks.feed_forward_gaussian, networks.recurrent_gaussian
for network in nets:
config = self._define_config()
with config.unlocked:
config.env = 'Pendulum-v0'
config.max_length = 200
config.steps = 500
config.network = network
for score in train.train(config, env_processes=True):
float(score)
def test_no_crash_variable_duration(self):
config = self._define_config()
with config.unlocked:
config.env = functools.partial(tools.MockEnvironment,
observ_shape=(2, 3),
action_shape=(3, ),
min_duration=5,
max_duration=25)
config.max_length = 25
config.steps = 100
config.network = networks.recurrent_gaussian
for score in train.train(config, env_processes=False):
float(score)
def test_no_crash_observation_shape(self):
nets = networks.feed_forward_gaussian, networks.recurrent_gaussian
observ_shapes = (1,), (2, 3), (2, 3, 4)
for network, observ_shape in itertools.product(nets, observ_shapes):
config = self._define_config()
with config.unlocked:
config.env = functools.partial(
tools.MockEnvironment, observ_shape, action_shape=(3,),
min_duration=15, max_duration=15)
config.max_length = 20
config.steps = 50
config.network = network
for score in train.train(config, env_processes=False):
float(score)
def test_no_crash_observation_shape(self):
nets = networks.feed_forward_gaussian, networks.recurrent_gaussian
observ_shapes = (1, ), (2, 3), (2, 3, 4)
for network, observ_shape in itertools.product(nets, observ_shapes):
config = self._define_config()
with config.unlocked:
config.env = functools.partial(tools.MockEnvironment,
observ_shape,
action_shape=(3, ),
min_duration=15,
max_duration=15)
config.max_length = 20
config.steps = 50
config.network = network
for score in train.train(config, env_processes=False):
float(score)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'exp_path',
type=str,
nargs='*',
help='Full experiment path (to the dir where the config is stored)')
parser.add_argument(
'-nep',
'--no_env_process',
default=False,
action='store_true',
help='Step environments in separate processes to circumvent the GIL')
parser.add_argument('-r',
'--render',
default=False,
action='store_true',
help='Whether to render the run')
parser.add_argument('-c',
'--cpu',
default=False,
action='store_true',
help='Whether to run the training on access1-cp')
parser.add_argument('-e',
'--edgar',
default=False,
action='store_true',
help='Whether to run the training on edgar')
parser.add_argument('-b',
'--besteffort',
default=False,
action='store_true',
help='Whether to run in besteffort mode')
parser.add_argument('-nc',
'--nb_cores',
type=int,
default=8,
help='Number of cores to be used on the cluster')
parser.add_argument('-w',
'--wallclock',
type=int,
default=72,
help='Job wall clock time to be set on the cluster')
parser.add_argument(
'-s',
'--steps',
type=int,
default=None,
help=
'Number of steps of the experiment (if not None, change the config)')
args = parser.parse_args()
sys_path_clean = utils.get_sys_path_clean()
seed_path, timestamp_dir = os.path.split(os.path.normpath(
args.exp_path[0]))
exp_path, _ = os.path.split(os.path.normpath(seed_path))
exp_name = os.path.basename(exp_path)
rendered_envs_path = '/home/thoth/apashevi/scratch_remote/Cache/Code/{}/rlgrasp/rendered_envs.py'.format(
exp_name)
if not args.cpu and not args.edgar:
# run the job locally
utils.change_sys_path(sys_path_clean, exp_path)
import agents.scripts.train as trainer
from agents.scripts import utility
assert len(args.exp_path) == 1
config = utility.load_config(args.exp_path[0])
with config.unlocked:
config.num_agents = 4
if args.steps is not None:
config.steps = args.steps
utils.rewrite_rendered_envs_file(args.render, rendered_envs_path)
for score in trainer.train(config, not args.no_env_process):
print('Score {}'.format(score))
if args.render:
utils.rewrite_rendered_envs_file(False, rendered_envs_path)
else:
if args.edgar:
cluster = 'edgar'
else:
cluster = 'access1-cp'
utils.rewrite_rendered_envs_file(False, rendered_envs_path)
job_cluster = utils.get_job(cluster, args.besteffort, args.nb_cores,
args.wallclock)
timestamp = timestamp_dir.split('-')[0]
config = timestamp_dir.split('-')[1]
if len(args.exp_path) == 1:
send_job(job_cluster, seed_path, timestamp, config, args_steps)
else:
for exp_path_complete in args.exp_path:
seed_path, _ = os.path.split(
os.path.normpath(exp_path_complete))
send_job(job_cluster, seed_path, timestamp, config, args.steps)