def test_can_create_two_rats(self):
rat1 = rodent.Rat(name='rat1')
rat2 = rodent.Rat(name='rat2')
arena = corr_arenas.EmptyCorridor()
arena.add_free_entity(rat1)
arena.add_free_entity(rat2)
mjcf.Physics.from_mjcf_model(arena.mjcf_model) # Should not raise an error.
rat1.mjcf_model.model = 'rat3'
rat2.mjcf_model.model = 'rat4'
mjcf.Physics.from_mjcf_model(arena.mjcf_model) # Should not raise an error.
def rodent_run_gaps(random_state=None):
"""Requires a rodent to run down a corridor with gaps."""
# Build a position-controlled rodent walker.
walker = rodent.Rat(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a corridor-shaped arena with gaps, where the sizes of the gaps and
# platforms are uniformly randomized.
arena = corr_arenas.GapsCorridor(platform_length=distributions.Uniform(
.4, .8),
gap_length=distributions.Uniform(.05, .2),
corridor_width=2,
corridor_length=40,
aesthetic='outdoor_natural')
# Build a task that rewards the agent for running down the corridor at a
# specific velocity.
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
target_velocity=1.0,
contact_termination=False,
terminate_at_height=-0.3,
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 rodent_two_touch(random_state=None):
"""Requires a rodent to tap an orb, wait an interval, and tap it again."""
# Build a position-controlled rodent walker.
walker = rodent.Rat(
observable_options={'egocentric_camera': dict(enabled=True)})
arena = floors.Floor(size=(10., 10.), aesthetic='outdoor_natural')
task = reach.TwoTouch(
walker=walker,
arena=arena,
target_builders=[
functools.partial(target_sphere.TargetSphereTwoTouch,
radius=0.025),
],
randomize_spawn_rotation=True,
target_type_rewards=[25.],
shuffle_target_builders=False,
target_area=(1.5, 1.5),
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 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()
arena = floors.Floor(
size=(10., 10.),
aesthetic='outdoor_natural')
task = reach.TwoTouch(
walker=walker,
arena=arena,
target_builders=[
functools.partial(target_sphere.TargetSphereTwoTouch, radius=0.025),
],
randomize_spawn_rotation=True,
target_type_rewards=[25.],
shuffle_target_builders=False,
target_area=(1.5, 1.5),
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 _get_rat_corridor_physics():
walker = rodent.Rat()
arena = corr_arenas.EmptyCorridor()
task = corr_tasks.RunThroughCorridor(walker=walker,
arena=arena,
walker_spawn_position=(5, 0, 0),
walker_spawn_rotation=0,
physics_timestep=_PHYSICS_TIMESTEP,
control_timestep=_CONTROL_TIMESTEP)
env = composer.Environment(time_limit=30,
task=task,
strip_singleton_obs_buffer_dim=True)
return walker, env
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 rodent_maze_forage(random_state=None):
"""Requires a rodent to find all items in a maze."""
# Build a position-controlled rodent walker.
walker = rodent.Rat(
observable_options={'egocentric_camera': dict(enabled=True)})
# Build a maze with rooms and targets.
wall_textures = labmaze_textures.WallTextures(style='style_01')
arena = mazes.RandomMazeWithTargets(x_cells=11,
y_cells=11,
xy_scale=.5,
z_height=.3,
max_rooms=4,
room_min_size=4,
room_max_size=5,
spawns_per_room=1,
targets_per_room=3,
wall_textures=wall_textures,
aesthetic='outdoor_natural')
# Build a task that rewards the agent for obtaining targets.
task = random_goal_maze.ManyGoalsMaze(walker=walker,
maze_arena=arena,
target_builder=functools.partial(
target_sphere.TargetSphere,
radius=0.05,
height_above_ground=.125,
rgb1=(0, 0, 0.4),
rgb2=(0, 0, 0.7)),
target_reward_scale=50.,
contact_termination=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 test_set_name(self): name = 'fred' walker = rodent.Rat(name=name) self.assertEqual(walker.mjcf_model.model, name)
def test_get_element_tuple_property(self, name):
attribute_value = getattr(rodent.Rat(), name)
self.assertNotEmpty(attribute_value)
for item in attribute_value:
self.assertIsInstance(item, mjcf.Element)
def test_get_element_property(self, name): attribute_value = getattr(rodent.Rat(), name) self.assertIsInstance(attribute_value, mjcf.Element)
def test_proprioception(self):
walker = rodent.Rat()
for item in walker.observables.proprioception:
self.assertIsInstance(item, observable_base.Observable)
