def _update_observation(self):
return_obs = Observation()
return_obs.doubleArray = np.zeros(MDPState.nfeatures)
for effector, mapping in MDPState.description.iteritems():
pos = NaoWorldModel().get_effector_pos(effector)
for key, axis in mapping.iteritems():
return_obs.doubleArray[axis] = pos[MDPState.key_to_index(key)]
return return_obs
def init(self, taskspec):
super(PenaltyKickAgent, self).init(taskspec)
ts = TaskSpecParser(taskspec)
if ts.valid:
extra = ts.get_extra()
v = ['FEATUREREP', 'SUPPORTLEG', 'STATEDESCR', 'ACTIONDESCR', 'COPYRIGHT']
pos = []
for i, id_ in enumerate(list(v)):
try:
pos.append(extra.index(id_))
except:
v.remove(id_)
sorted_v = sorted(zip(pos, v))
v = [s[1] for s in sorted_v]
for i, id_ in enumerate(v):
val = ts.get_value(i, extra, v)
if id_ == 'FEATUREREP':
self._feature_rep = val
if id_ == 'SUPPORTLEG':
if val == 'left':
self._ankle_roll = "RAnkleRoll"
self._hip_roll = "RHipRoll"
min_hip_roll, max_hip_roll = NaoWorldModel().get_robot_info(self._hip_roll)
leg_length = NaoWorldModel().get_robot_info("TibiaLength") + NaoWorldModel().get_robot_info("ThighLength")
MDPState.dtype = MDPState.DTYPE_INT
if self._feature_rep == 'rl':
try:
max_location = NaoWorldModel().get_object("ball").resolution[0]
except AttributeError:
max_location = 0
MDPState.set_minmax_features([0, max_location],
[math.floor(leg_length * math.sin(min_hip_roll)),
math.ceil(leg_length * math.sin(max_hip_roll))])
MDPState.set_states_per_dim([int((MDPState.max_features[0] - MDPState.min_features[0]) / 2),
int(math.ceil((MDPState.max_features[1] - MDPState.min_features[1]) / 4))])
# noinspection PyShadowingNames
def is_valid(self):
real_state = True
if MDPState.min_features is not None:
for (feature, min_feature, max_feature) in zip(self, MDPState.min_features,
MDPState.max_features):
if feature < (min_feature - eps) or feature > (max_feature + eps):
real_state = False
self._logger.debug("\t\t\t\tNext state is not valid (feature %d out of range)", feature)
break
return real_state
MDPState.is_valid = is_valid
else:
MDPState.set_minmax_features([math.floor(leg_length * math.sin(min_hip_roll)),
math.ceil(leg_length * math.sin(max_hip_roll))])
MDPState.set_nfeatures(
int(math.ceil((MDPState.max_features - MDPState.min_features + 1) / self._bin_width)))
# noinspection PyShadowingNames
def is_valid(self):
num_ones = len(np.where(self.get()[0:len(self)] == 1)[0])
if num_ones > 1 or num_ones < 1 or not all(i == 0 or i == 1 for i in self.get()):
return False
return True
# noinspection PyShadowingNames
def encode(self):
return np.where(self.get()[0:len(self)] == 1)[0]
def decode(cls, state_repr):
decoded = [0] * cls.nfeatures
bin_num = 0
if isinstance(state_repr[0], int):
bin_num = state_repr[0]
elif isinstance(state_repr[0], float):
bin_num = int(math.floor((state_repr[0] - cls.min_features) / self._bin_width))
if 0 <= bin_num <= cls.nfeatures - 1:
decoded[bin_num] = 1
elif bin_num < 0:
decoded[0] = 1
else:
decoded[cls.nfeatures - 1] = 1
return cls(decoded)
MDPState.is_valid = is_valid
MDPState.encode = encode
MDPState.decode = decode
