where, $b_{light}$ is a boolean variable equal to 1 if the light intensity is above a certain threshold. The average of the values returned from the LS at different positions in the arena while the light is ON, forms the threshold. $f_{antipuck}$ models the repulsion of robot when it encounters a puck.

We carried out two sets of experiments - 1) Real-robot 2) Energy Saving. In the real-robot experiment, we switched off the sharing module of our proposed algorithm and used only a single real-robot to learn the respective tasks in the three scenarios explained later. In addition, we also used a set of three real robots to learn the same tasks with the sharing module enabled. This allowed the robots to share their subcontrollers (antibodies) mutually. The second set of experiments were designed with a completely different motive. These experiments were intended to introduce and test a new and energy efficient sharing mechanism. Experiments herein were carried out in an emulated environment with 80 soft or virtual robots connected through a dynamic network. Besides, we also performed experiments wherein we attempt to evolve single robot controllers for each of the scenarios. Though not crucial, this was necessary to validate 