Environment/LASROMEnv.py
def _reward_actuator_intensity(self, observation):
        """
        This reward function is used in non-interactive model and only for testing and tuning hyper-parameters of LASAgent.
        The reward is sum of the intensity of light.
        Maybe we will add intensity of SMA into calculating the rewards later.

        """
        reward = 0.0
        light_color_start_index = self.prox_sensor_num
        red_light_index = []
        for i in range(self.lights_num):
            R = observation[light_color_start_index + i * 3]
            G = observation[light_color_start_index + i * 3 + 1]
            B = observation[light_color_start_index + i * 3 + 2]
            reward += R

        return reward

        This reward function is used in non-interactive model and only for testing and tuning hyper-parameters of LASAgent.
        The reward is sum of the intensity of light.
        Maybe we will add intensity of SMA into calculating the rewards later.

        
This reward function is used in non-interactive model and only for testing and tuning hyper-parameters of LASAgent.
The reward is sum of the intensity of light.
Maybe we will add intensity of SMA into calculating the rewards later.
This reward function is used in non - interactive model and only for testing and tuning hyper - parameters of LASAgent . The reward is sum of the intensity of light . Maybe we will add intensity of SMA into calculating the rewards later .
def _reward_actuator_intensity(self, observation):
        reward = 0.0
        light_color_start_index = self.prox_sensor_num
        red_light_index = []
        for i in range(self.lights_num):
            R = observation[light_color_start_index + i * 3]
            G = observation[light_color_start_index + i * 3 + 1]
            B = observation[light_color_start_index + i * 3 + 2]
            reward += R
        return reward
def _reward_actuator_intensity ( self , observation ) : reward = 0.0 light_color_start_index = self . prox_sensor_num red_light_index = [ ] for i in range ( self . lights_num ) : R = observation [ light_color_start_index + i * 3 ] G = observation [ light_color_start_index + i * 3 + 1 ] B = observation [ light_color_start_index + i * 3 + 2 ] reward += R return reward
This reward function is used in non-interactive model and only for testing and tuning hyper-parameters of LASAgent.
This reward function is used in non - interactive model and only for testing and tuning hyper - parameters of LASAgent .
"""
        This reward function is used in non-interactive model and only for testing and tuning hyper-parameters of LASAgent.
        The reward is sum of the intensity of light.
        Maybe we will add intensity of SMA into calculating the rewards later.

        """
self
observation