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