def test_termination_and_discount(self):
walker = cmu_humanoid.CMUHumanoid()
arena = floors.Floor()
task = go_to_target.GoToTarget(walker=walker, arena=arena)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
env.reset()
zero_action = np.zeros_like(env.physics.data.ctrl)
# Walker starts in upright position.
# Should not trigger failure termination in the first few steps.
for _ in range(5):
env.step(zero_action)
self.assertFalse(task.should_terminate_episode(env.physics))
np.testing.assert_array_equal(task.get_discount(env.physics), 1)
# Rotate the walker upside down and run the physics until it makes contact.
current_time = env.physics.data.time
walker.shift_pose(env.physics,
position=(0, 0, 10),
quaternion=(0, 1, 0, 0))
env.physics.forward()
while env.physics.data.ncon == 0:
env.physics.step()
env.physics.data.time = current_time
# Should now trigger a failure termination.
env.step(zero_action)
self.assertTrue(task.should_terminate_episode(env.physics))
np.testing.assert_array_equal(task.get_discount(env.physics), 0)
def test_reward_fixed_target(self):
walker = cmu_humanoid.CMUHumanoid()
arena = floors.Floor()
task = go_to_target.GoToTarget(walker=walker,
arena=arena,
moving_target=False)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
env.reset()
target_position = task.target_position(env.physics)
zero_action = np.zeros_like(env.physics.data.ctrl)
for _ in range(2):
timestep = env.step(zero_action)
self.assertEqual(timestep.reward, 0)
walker_pos = env.physics.bind(walker.root_body).xpos
walker.set_pose(
env.physics,
position=[target_position[0], target_position[1], walker_pos[2]])
env.physics.forward()
# Receive reward while the agent remains at that location.
timestep = env.step(zero_action)
self.assertEqual(timestep.reward, 1)
# Target position should not change.
np.testing.assert_array_equal(target_position,
task.target_position(env.physics))
def test_observables(self):
walker = cmu_humanoid.CMUHumanoid()
arena = floors.Floor()
task = go_to_target.GoToTarget(walker=walker,
arena=arena,
moving_target=False)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
timestep = env.reset()
self.assertIn('walker/target', timestep.observation)
def test_target_position_randomized_on_reset(self):
walker = cmu_humanoid.CMUHumanoid()
arena = floors.Floor()
task = go_to_target.GoToTarget(walker=walker,
arena=arena,
moving_target=False)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
env.reset()
first_target_position = task.target_position(env.physics)
env.reset()
second_target_position = task.target_position(env.physics)
self.assertFalse(
np.all(first_target_position == second_target_position),
'Target positions are unexpectedly identical.')
def jumping_ball_go_to_target(random_state=None):
walker = jumping_ball.JumpingBallWithHead()
# Build a standard floor arena.
arena = floors.Floor()
# Build a task that rewards the agent for going to a target.
task = go_to_target.GoToTarget(walker=walker,
arena=arena,
sparse_reward=False,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def cmu_humanoid_go_to_target(random_state=None):
"""Requires a CMU humanoid to go to a target."""
# Build a position-controlled CMU humanoid walker.
walker = cmu_humanoid.CMUHumanoidPositionControlled()
# Build a standard floor arena.
arena = floors.Floor()
# Build a task that rewards the agent for going to a target.
task = go_to_target.GoToTarget(walker=walker,
arena=arena,
physics_timestep=0.005,
control_timestep=0.03)
return composer.Environment(time_limit=30,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def _build_task(**task_kwargs):
walker = cmu_humanoid.CMUHumanoid()
arena = floors.Floor()
task = go_to_target.GoToTarget(walker=walker, arena=arena, **task_kwargs)
return task
