def _reset(self):
if self.physicsClientId < 0:
self.ownsPhysicsClient = True
if self.isRender:
self._p = bullet_client.BulletClient(
connection_mode=pybullet.GUI)
else:
self._p = bullet_client.BulletClient()
self.physicsClientId = self._p._client
self._p.configureDebugVisualizer(pybullet.COV_ENABLE_GUI, 0)
if self.scene is None:
self.scene = self.create_single_player_scene(self._p)
if not self.scene.multiplayer and self.ownsPhysicsClient:
self.scene.episode_restart(self._p)
self.robot.scene = self.scene
self.frame = 0
self.done = 0
self.reward = 0
dump = 0
s = self.robot.reset(self._p)
self.potential = self.robot.calc_potential()
return s
def __init__(
self,
urdfRoot=pybullet_data.getDataPath(),
actionRepeat=5,
isEnableSelfCollision=True,
isDiscrete=False,
renders=False,
): # just change to True if you want to visualize
print("init")
self._timeStep = 0.01
self._urdfRoot = urdfRoot
self._actionRepeat = actionRepeat
self._isEnableSelfCollision = isEnableSelfCollision
self._observation = []
self._ballUniqueId = -1
self._envStepCounter = 0
self._renders = renders
# self._renders = True
self._isDiscrete = isDiscrete
if self._renders:
self._p = bullet_client.BulletClient(connection_mode=pybullet.GUI)
else:
self._p = bullet_client.BulletClient()
self.seed()
# self.reset()
observationDim = 7 # len(self.getExtendedObservation())
# print("observationDim")
# print(observationDim)
# observation_high = np.array([np.finfo(np.float32).max] * observationDim)
observation_high = np.ones(observationDim) * 1000 # np.inf
if isDiscrete:
self.action_space = spaces.Discrete(9)
else:
action_dim = 2
self._action_bound = 1
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high,
action_high,
dtype=np.float32)
self.observation_space = spaces.Box(-observation_high,
observation_high,
dtype=np.float32)
self.viewer = None
self.goal = []
self.useSphere = False
self.dist_to_goal_threshold = 0.2 # threshold to consider we "got to goal"
self._hard_reset = True
self.reset()
def __init__(self,
urdfRoot=pybullet_data.getDataPath(),
actionRepeat=50,
isEnableSelfCollision=True,
isDiscrete=False,
renders=False,
length=1,
deterministic=True,
r_type='neg_dist',
lod=0):
self._timeStep = 0.01
self._urdfRoot = urdfRoot
self._lod = lod
if lod > 0:
actionRepeat = 10
self._actionRepeat = actionRepeat
self._isEnableSelfCollision = isEnableSelfCollision
self._observation = []
self._ballUniqueId = -1
self._envStepCounter = 0
self._renders = renders
self._isDiscrete = isDiscrete
if self._renders:
self._p = bullet_client.BulletClient(
connection_mode=pybullet.GUI)
else:
self._p = bullet_client.BulletClient()
self._seed()
#self.reset()
observationDim = 5
observation_high = np.ones(observationDim) * 1000 #np.inf
if (isDiscrete):
self.action_space = spaces.Discrete(3)
else:
action_dim = 2
self._action_bound = 1
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(-observation_high, observation_high)
self.viewer = None
self.length = length
self.deterministic = deterministic
self.switch = None
self.max_x, self.min_y, self.max_y = None, None, None
self.r_type = r_type
def __init__(self, map_id=1):
self.p = bullet_client.BulletClient(connection_mode=pybullet.DIRECT)
self.map_id = map_id
self.cars = []
self.position_cars = []
# variable for tracking
self.n_collision = 0
self.step_count = 0
self.n_reset = 0
# reset
self._reset()
def __init__(self,pybullet_sim_factory = boxstack_pybullet_sim,
render=True,
render_sleep=False,
debug_visualization=True,
hard_reset = False,
render_width=240,
render_height=240,
action_repeat=1,
time_step = 1./240.,
num_bullet_solver_iterations=50,
urdf_root=pybullet_data.getDataPath()):
"""Initialize the gym environment.
Args:
urdf_root: The path to the urdf data folder.
"""
self._pybullet_sim_factory = pybullet_sim_factory
self._time_step = time_step
self._urdf_root = urdf_root
self._observation = []
self._action_repeat=action_repeat
self._num_bullet_solver_iterations = num_bullet_solver_iterations
self._env_step_counter = 0
self._is_render = render
self._debug_visualization = debug_visualization
self._render_sleep = render_sleep
self._render_width=render_width
self._render_height=render_height
self._cam_dist = .3
self._cam_yaw = 50
self._cam_pitch = -35
self._hard_reset = True
self._last_frame_time = 0.0
optionstring='--width={} --height={}'.format(render_width,render_height)
print("urdf_root=" + self._urdf_root)
if self._is_render:
self._pybullet_client = bullet_client.BulletClient(
connection_mode=pybullet.GUI, options=optionstring)
else:
self._pybullet_client = bullet_client.BulletClient()
if (debug_visualization==False):
self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_GUI,enable=0)
self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_RGB_BUFFER_PREVIEW,enable=0)
self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_DEPTH_BUFFER_PREVIEW,enable=0)
self._pybullet_client.configureDebugVisualizer(flag=self._pybullet_client.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,enable=0)
self._pybullet_client.setAdditionalSearchPath(urdf_root)
self._seed()
self.reset()
observation_high = (
self._example_sim.GetObservationUpperBound())
observation_low = (
self._example_sim.GetObservationLowerBound())
action_dim = self._example_sim.GetActionDimension()
self._action_bound = 1
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(observation_low, observation_high)
self.viewer = None
self._hard_reset = hard_reset # This assignment need to be after reset()
def __init__(
self,
urdf_root=pybullet_data.getDataPath(),
action_repeat=1,
distance_weight=1.0,
energy_weight=0.005,
shake_weight=0.0,
drift_weight=0.0,
distance_limit=float("inf"),
observation_noise_stdev=0.0,
self_collision_enabled=True,
motor_velocity_limit=np.inf,
pd_control_enabled=False, #not needed to be true if accurate motor model is enabled (has its own better PD)
leg_model_enabled=True,
accurate_motor_model_enabled=True,
motor_kp=1.0,
motor_kd=0.02,
torque_control_enabled=False,
motor_overheat_protection=True,
hard_reset=True,
on_rack=False,
render=False,
kd_for_pd_controllers=0.3,
env_randomizer=minitaur_env_randomizer.MinitaurEnvRandomizer()):
"""Initialize the minitaur gym environment.
Args:
urdf_root: The path to the urdf data folder.
action_repeat: The number of simulation steps before actions are applied.
distance_weight: The weight of the distance term in the reward.
energy_weight: The weight of the energy term in the reward.
shake_weight: The weight of the vertical shakiness term in the reward.
drift_weight: The weight of the sideways drift term in the reward.
distance_limit: The maximum distance to terminate the episode.
observation_noise_stdev: The standard deviation of observation noise.
self_collision_enabled: Whether to enable self collision in the sim.
motor_velocity_limit: The velocity limit of each motor.
pd_control_enabled: Whether to use PD controller for each motor.
leg_model_enabled: Whether to use a leg motor to reparameterize the action
space.
accurate_motor_model_enabled: Whether to use the accurate DC motor model.
motor_kp: proportional gain for the accurate motor model.
motor_kd: derivative gain for the accurate motor model.
torque_control_enabled: Whether to use the torque control, if set to
False, pose control will be used.
motor_overheat_protection: Whether to shutdown the motor that has exerted
large torque (OVERHEAT_SHUTDOWN_TORQUE) for an extended amount of time
(OVERHEAT_SHUTDOWN_TIME). See ApplyAction() in minitaur.py for more
details.
hard_reset: Whether to wipe the simulation and load everything when reset
is called. If set to false, reset just place the minitaur back to start
position and set its pose to initial configuration.
on_rack: Whether to place the minitaur on rack. This is only used to debug
the walking gait. In this mode, the minitaur's base is hanged midair so
that its walking gait is clearer to visualize.
render: Whether to render the simulation.
kd_for_pd_controllers: kd value for the pd controllers of the motors
env_randomizer: An EnvRandomizer to randomize the physical properties
during reset().
"""
self._time_step = 0.01
self._action_repeat = action_repeat
self._num_bullet_solver_iterations = 300
self._urdf_root = urdf_root
self._self_collision_enabled = self_collision_enabled
self._motor_velocity_limit = motor_velocity_limit
self._observation = []
self._env_step_counter = 0
self._is_render = render
self._last_base_position = [0, 0, 0]
self._distance_weight = distance_weight
self._energy_weight = energy_weight
self._drift_weight = drift_weight
self._shake_weight = shake_weight
self._distance_limit = distance_limit
self._observation_noise_stdev = observation_noise_stdev
self._action_bound = 1
self._pd_control_enabled = pd_control_enabled
self._leg_model_enabled = leg_model_enabled
self._accurate_motor_model_enabled = accurate_motor_model_enabled
self._motor_kp = motor_kp
self._motor_kd = motor_kd
self._torque_control_enabled = torque_control_enabled
self._motor_overheat_protection = motor_overheat_protection
self._on_rack = on_rack
self._cam_dist = 1.0
self._cam_yaw = 0
self._duckId = -1
self._cam_pitch = -30
self._hard_reset = True
self._kd_for_pd_controllers = kd_for_pd_controllers
self._last_frame_time = 0.0
print("urdf_root=" + self._urdf_root)
self._env_randomizer = env_randomizer
# PD control needs smaller time step for stability.
if pd_control_enabled or accurate_motor_model_enabled:
self._time_step /= NUM_SUBSTEPS
self._num_bullet_solver_iterations /= NUM_SUBSTEPS
self._action_repeat *= NUM_SUBSTEPS
if self._is_render:
self._pybullet_client = bullet_client.BulletClient(
connection_mode=pybullet.GUI)
else:
self._pybullet_client = bullet_client.BulletClient()
self._seed()
self.reset()
observation_high = (self.minitaur.GetObservationUpperBound() +
OBSERVATION_EPS)
observation_low = (self.minitaur.GetObservationLowerBound() -
OBSERVATION_EPS)
action_dim = 8
action_high = np.array([self._action_bound] * action_dim)
self.action_space = spaces.Box(-action_high, action_high)
self.observation_space = spaces.Box(observation_low, observation_high)
self.viewer = None
self._hard_reset = hard_reset # This assignment need to be after reset()
def __init__(self,
urdf_path,
action_repeat=1,
distance_weight=1.0,
energy_weight=0.005,
drift_weight=0.002,
render=False):
"""Environment for the T-rex model.
:param urdf_path: path to the urdf data folder.
:param action_repeat: number of simulation steps before actions are applied.
:param distance_weight: weight of the distance term in the reward.
:param energy_weight: weight of the energy term in the reward.
:param render: whether to render the simulation.
"""
self._time_step = 0.01
self._urdf_path = urdf_path
self._action_repeat = action_repeat
self._num_bullet_solver_iterations = 300
self._observation = []
self._env_step_counter = 0
self._is_render = render
self._last_base_position = [0.] * 3
self._weight_distance = distance_weight
self._weight_energy = energy_weight
self._weight_drift = drift_weight
self._action_bound = 1
self._cam_dist = 10.0
self._cam_yaw = 90.0
self._cam_pitch = -30.0
self._last_frame_time = 0.0
# PD control needs smaller time step for stability.
self._time_step /= NUM_SUBSTEPS
self._num_bullet_solver_iterations /= NUM_SUBSTEPS
self._action_repeat *= NUM_SUBSTEPS
connection_mode = pybullet.DIRECT
if self._is_render:
connection_mode = pybullet.GUI
self._pybullet_client = bullet_client.BulletClient(
connection_mode=connection_mode)
self._starting_configuration = {
'femur_L_joint': -0.6,
'tibia_L_joint': 0.4,
'tarsometatarsus_L_joint': -1.2,
'femur_R_joint': -0.6,
'tibia_R_joint': 0.4,
'tarsometatarsus_R_joint': -1.2
}
self.model = None
self.np_random = None
self.seed()
self.reset()
action_low, action_high = self.model.get_action_limits()
self.action_space = spaces.Box(low=action_low,
high=action_high,
dtype=np.float32)
observation_low, observation_high = self.model.get_observation_limits()
self.observation_space = spaces.Box(low=observation_low,
high=observation_high,
dtype=np.float32)
