def test_all_does_not_modify_actions(self, domain_name, task_name):
env = DmControlEnv(domain_name, task_name)
a = env.action_space.sample()
a_copy = copy(a)
env.step(a)
if isinstance(a, collections.Iterable):
self.assertEquals(a.all(), a_copy.all())
else:
self.assertEquals(a, a_copy)
def test_can_step_and_render(self, domain_name, task_name):
env = DmControlEnv(domain_name, task_name)
ob_space = env.observation_space
act_space = env.action_space
ob = env.reset()
assert ob_space.contains(ob)
a = act_space.sample()
assert act_space.contains(a)
step_env(env, n=10, render=True)
def run_task(domain_name, task_name):
print("run: domain %s task %s" % (domain_name, task_name))
dm_control_env = normalize(
DmControlEnv(
domain_name=domain_name,
task_name=task_name,
plot=True,
width=600,
height=400),
normalize_obs=False,
normalize_reward=False)
time_step = dm_control_env.reset()
action_spec = dm_control_env.action_space
for _ in range(5):
dm_control_env.render()
action = action_spec.sample()
next_obs, reward, done, info = dm_control_env.step(action)
if done:
break
dm_control_env.close()
def test_dm_control_theano_policy(self):
task = ALL_TASKS[0]
env = TheanoEnv(DmControlEnv(domain_name=task[0], task_name=task[1]))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=10,
max_path_length=5,
n_itr=1,
discount=0.99,
step_size=0.01,
)
algo.train()
def test_pickling(self, domain_name, task_name):
env = DmControlEnv(domain_name, task_name)
round_trip = pickle.loads(pickle.dumps(env))
assert round_trip
step_env(round_trip)
def __init__(self, method_name='runTest', param=ALL_TASKS[0]):
super().__init__(method_name)
self.env = DmControlEnv(domain_name=param[0], task_name=param[1])
