def call_reward_function()

in simulation_ws/src/rl-agent/markov/environments/mars_env.py [0:0]

• 28 lines of code
• 7 McCabe index (conditional complexity)

    def call_reward_function(self, action):
        self.get_distance_to_object() #<-- Also evaluate for sideswipe and collistion damage
        
        # Get the observation
        self.set_next_state()
        
        # reduce power supply range
        self.power_supply_range = MAX_STEPS - self.steps
        
        # calculate reward
        reward, done = self.reward_function()

        # Accumulate reward for the episode
        self.reward_in_episode += reward

        # Get average Imu reading
        if self.max_lin_accel_x > 0 or self.max_lin_accel_y > 0 or self.max_lin_accel_z > 0:
            avg_imu = (self.max_lin_accel_x + self.max_lin_accel_y + self.max_lin_accel_y) / 3
        else:
            avg_imu = 0
    
        print('Step:%.2f' % self.steps,
              'Steering:%f' % action[0],
              'R:%.2f' % reward,                                # Reward
              'DTCP:%f' % self.current_distance_to_checkpoint,  # Distance to Check Point
              'DT:%f' % self.distance_travelled,                # Distance Travelled
              'CT:%.2f' % self.collision_threshold,             # Collision Threshold
              'CTCP:%f' % self.closer_to_checkpoint,            # Is closer to checkpoint
              'PSR: %f' % self.power_supply_range,              # Steps remaining in Episode
              'IMU: %f' % avg_imu)

        self.reward = reward
        self.done = done

        self.last_position_x = self.x
        self.last_position_y = self.y