def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0:
self._flag_reposition()
self.flag_timeout -= 1
return state, reward, done, meta
def _step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: The angles, velocities and torques of all motors.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
"""
#print("Env apply raw action", action)
action = self._transform_action_to_motor_command(action)
#print("Env apply action", action)
#for _ in range(self._action_repeat):
# self.robot.ApplyAction(action)
# pybullet.stepSimulation()
for i in range(len(self.Amax)):
if action[i] > self.Amax[i]:
self.Amax[i] = action[i]
#print("Action max", self.Amax)
for _ in range(self.action_repeat):
state = CameraRobotEnv._step(self, action)
return state
def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0 or (self.flag_timeout < 225
and self.robot.walk_target_dist < 0.4):
if self.robot.walk_target_dist < 0.4:
self.waypoint = 1
self._flag_reposition()
self.flag_timeout -= 1
depth_size = 16
if "depth" in self.config["output"]:
depth_obs = self.get_observations()["depth"]
x_start = int(self.windowsz / 2 - depth_size)
x_end = int(self.windowsz / 2 + depth_size)
y_start = int(self.windowsz / 2 - depth_size)
y_end = int(self.windowsz / 2 + depth_size)
self.obstacle_dist = (np.mean(depth_obs[x_start:x_end,
y_start:y_end, -1]))
# state, reward, done, meta = CameraRobotEnv._step(self, a)
# if self.flag_timeout <= 0 or (self.flag_timeout < FLAG_LIMIT and self.robot.walk_target_dist < 0.8):
# self._flag_reposition()
# self.flag_timeout -= 1
debug = 0
if debug:
print(
"Frame: {}, FlagTimeOut: {}, Reward: {:.3f}, Distance: {:.3f}, "
.format(self.nframe, self.flag_timeout, reward,
self.robot.walk_target_dist), done)
return state, reward, done, meta
def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0 or (self.flag_timeout < 225
and self.robot.walk_target_dist < 0.8):
self._flag_reposition()
self.flag_timeout -= 1
self.obstacle_dist = (np.mean(state[16:48, 16:48, -1]))
return state, reward, done, meta
def _step(self, a):
state, reward, done, meta = CameraRobotEnv._step(self, a)
if self.flag_timeout <= 0 or (self.flag_timeout < 225 and self.robot.walk_target_dist < 0.8):
self._flag_reposition()
self.flag_timeout -= 1
if "depth" in self.config["output"]:
depth_obs = self.get_observations()["depth"]
x_start = int(self.windowsz/2-16)
x_end = int(self.windowsz/2+16)
y_start = int(self.windowsz/2-16)
y_end = int(self.windowsz/2+16)
self.obstacle_dist = (np.mean(depth_obs[x_start:x_end, y_start:y_end, -1]))
return state, reward, done, meta