def expected_next_action_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[[31, 30, 33], [34, MISSING_VALUE, 35], [MISSING_VALUE, 36, 37]],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13], self.get_action_normalization_parameters()
)
return dense
def expected_action_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[[21, 20, MISSING_VALUE], [24, 23, 25], [27, MISSING_VALUE, 26]],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13], self.get_action_normalization_parameters()
)
return dense
def expected_action_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[[21, 20, MISSING_VALUE], [24, 23, 25], [27, MISSING_VALUE, 26]],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13],
self.get_action_normalization_parameters())
return dense
def expected_next_action_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[[31, 30, 33], [34, MISSING_VALUE, 35], [MISSING_VALUE, 36, 37]],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13],
self.get_action_normalization_parameters())
return dense
def expected_next_state_features(self, normalize):
# Feature order: 1, 3, 2, 4
dense = np.array(
[
[11, MISSING_VALUE, 10, MISSING_VALUE],
[13, MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[MISSING_VALUE, 15, 14, 16],
],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [1, 3, 2, 4], self.get_state_normalization_parameters())
return dense
def expected_next_state_features(self, normalize):
# Feature order: 1, 3, 2, 4
dense = np.array(
[
[11, MISSING_VALUE, 10, MISSING_VALUE],
[13, MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[MISSING_VALUE, 15, 14, 16],
],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [1, 3, 2, 4], self.get_state_normalization_parameters()
)
return dense
def expected_possible_next_actions_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[
[MISSING_VALUE, 40, MISSING_VALUE],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[41, MISSING_VALUE, MISSING_VALUE],
[45, 43, 44],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13], self.get_action_normalization_parameters()
)
return dense
def expected_possible_next_actions_features(self, normalize):
# Feature order: 12, 11, 13
dense = np.array(
[
[MISSING_VALUE, 40, MISSING_VALUE],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[41, MISSING_VALUE, MISSING_VALUE],
[45, 43, 44],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
[MISSING_VALUE, MISSING_VALUE, MISSING_VALUE],
],
dtype=np.float32,
)
if normalize:
dense = NumpyFeatureProcessor.preprocess_array(
dense, [12, 11, 13],
self.get_action_normalization_parameters())
return dense
