A Neuro-Evolution Approach to Shepherding Swarm Guidance in the Face of Uncertainty

Controlling a large swarm of agents is a challenging task. Shepherding refers to an active field of research that seeks to address this challenge by using a control agent (sheepdog), which guides a swarm (sheep) towards a goal. Traditional shepherding involves switching between two main behaviours: driving the swarm towards the goal, and collecting stray sheep back to the flock. Evidently, the movement of the agents are dependent on their sensed information. Therefore, effectively controlling a swarm is even more challenging when sensor information or communication channels are unreliable. In this paper, we propose a shepherding methodology to achieve efficient swarm control in the presence of noise in the sensed information. The proposed approach consists of a new resting behaviour and a neural network-based reinforcement learning model. The neural network is used to learn shepherding policies using the new resting behaviour, where the objective is to optimise the frequency of sheep-to-dog interactions with varying levels of noise. The proposed approach is validated through simulations. Numerical experiments show that the proposed approach results in a more effective and stable performance compared to some conventional shepherding models from the literature.