def test_contact(self):
walker = rodent.Rat()
# Build a corridor-shaped arena that is obstructed by walls.
arena = bowl.Bowl(size=(20., 20.), aesthetic='outdoor_natural')
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = escape.Escape(walker=walker,
arena=arena,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
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)
def test_observables(self):
walker = rodent.Rat()
# Build a corridor-shaped arena that is obstructed by walls.
arena = bowl.Bowl(size=(20., 20.), aesthetic='outdoor_natural')
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = escape.Escape(walker=walker,
arena=arena,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
random_state = np.random.RandomState(12345)
env = composer.Environment(task, random_state=random_state)
timestep = env.reset()
self.assertIn('walker/joints_pos', timestep.observation)
def rodent_escape_bowl(random_state=None):
"""Requires a rodent to climb out of a bowl-shaped terrain."""
# Build a position-controlled rodent walker.
walker = rodent.Rat(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a bowl-shaped arena.
arena = bowl.Bowl(size=(20., 20.), aesthetic='outdoor_natural')
# Build a task that rewards the agent for being far from the origin.
task = escape.Escape(walker=walker,
arena=arena,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(time_limit=20,
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)
def ant_escape_bowl(random_state=None):
walker = ant.Ant()
# Build a bowl-shaped arena.
arena = bowl.Bowl(ground_size=(15., 15.),
hfield_size=(30, 30, 5),
terrain_smoothness=0.15,
terrain_bump_scale=2.0)
# Build a task that rewards the agent for being far from the origin.
task = escape.Escape(walker=walker,
arena=arena,
walker_spawn_position=(0, 0, 1.5),
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
return composer.Environment(
time_limit=30, # 20
task=task,
random_state=random_state,
strip_singleton_obs_buffer_dim=True)