def ctor(scene, env_id=None):
return suite_dmlab.load(
scene=scene,
gym_env_wrappers=[
gym_wrappers.FrameGrayScale, gym_wrappers.FrameResize,
gym_wrappers.FrameStack
],
wrap_with_process=False)
def test_dmlab_env(self):
ctor = lambda: suite_dmlab.load(scene='lt_chasm',
gym_env_wrappers=[
wrappers.FrameGrayScale, wrappers.
FrameResize, wrappers.FrameStack
],
wrap_with_process=False)
self._env = parallel_py_environment.ParallelPyEnvironment([ctor] * 2)
env = tf_py_environment.TFPyEnvironment(self._env)
self.assertEqual((84, 84, 4), env.observation_spec().shape)
def test_process_env(self):
scene = 'lt_chasm'
self._env = suite_dmlab.load(
scene=scene,
gym_env_wrappers=[
gym_wrappers.FrameGrayScale, gym_wrappers.FrameResize,
gym_wrappers.FrameStack
],
wrap_with_process=True)
self.assertIsInstance(self._env, alf_environment.AlfEnvironment)
self.assertEqual((4, 84, 84), self._env.observation_spec().shape)
for _ in range(10):
actions = self._env.action_spec().sample()
self._env.step(actions)
def test_dmlab_env_run(self, scene):
ctor = lambda: suite_dmlab.load(scene=scene,
gym_env_wrappers=
[wrappers.FrameResize],
wrap_with_process=False)
self._env = parallel_py_environment.ParallelPyEnvironment([ctor] * 4)
env = tf_py_environment.TFPyEnvironment(self._env)
self.assertEqual((84, 84, 3), env.observation_spec().shape)
random_policy = random_tf_policy.RandomTFPolicy(
env.time_step_spec(), env.action_spec())
driver = dynamic_step_driver.DynamicStepDriver(env=env,
policy=random_policy,
observers=None,
num_steps=10)
driver.run(maximum_iterations=10)