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 = 200
config.network = networks.RecurrentGaussianPolicy
for score in train.train(config, env_processes=False):
float(score)
def test_no_crash_ant(self):
nets = networks.ForwardGaussianPolicy, networks.RecurrentGaussianPolicy
for network in nets:
config = self._define_config()
with config.unlocked:
config.env = 'Ant-v1'
config.max_length = 200
config.steps = 1000
config.network = network
for score in train.train(config, env_processes=True):
float(score)
def test_no_crash_ant(self):
nets = networks.ForwardGaussianPolicy, networks.RecurrentGaussianPolicy
for network in nets:
config = self._define_config()
with config.unlocked:
config.env = 'Ant-v1'
config.max_length = 200
config.steps = 1000
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 = 200
config.network = networks.RecurrentGaussianPolicy
for score in train.train(config, env_processes=False):
float(score)
def test_no_crash_observation_shape(self):
nets = networks.ForwardGaussianPolicy, networks.RecurrentGaussianPolicy
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 = 100
config.network = network
for score in train.train(config, env_processes=False):
float(score)
def test_no_crash_observation_shape(self):
nets = networks.ForwardGaussianPolicy, networks.RecurrentGaussianPolicy
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 = 100
config.network = network
for score in train.train(config, env_processes=False):
float(score)