Abstract
Abstract The field of swarm robotics has become more and more popular as a decentralised solution to problems of coordination among multiple independent agents. The recent developments in multi-agent reinforcement learning (MARL) have made it possible for agents to learn cooperative behaviours when operating in a dynamic environment and even outperform the traditional rule-based or heuristic coordination strategies. However, coordination in practical MARL-based swarms is still difficult, as many approaches are not scalable, have high communication cost, unstable coordination with high swarm density, and lack integration of efficiency, robustness, and adaptability. This paper introduces a multi-agent reinforcement learning framework for scalable and communication-efficient swarm coordination called RoboSwarmCoordAI, which surpasses the limitations of the above approaches. The proposed framework uses three major components: a state-encoding module that is aware of the coordination requirements, an adaptive neighbourhood-filtering module to avoid redundant inter-agent communication and a hybrid reward function that weights local robot goals with respect to the global swarm performance. RoboSwarmCoordAI uses a centralised training and decentralised execution approach where agents can leverage global information for training, but local information for execution. The framework was tested in simulation in cooperative exploration, distributed target search and task allocation scenarios. When evaluated within the range of simulations tested, RoboSwarmCoordAI outperformed baseline methods with a task success rate of 95.8%, 2.7 collisions per episode, and an efficiency score of 91.6. The analyses of scalability and communication efficiency also demonstrate the stable coordination performance up to 50 robots, at a lower communications cost. As a result, it was found that RoboSwarmCoordAI is a promising simulation-validated framework for adaptive swarm coordination, and future work will further validate RoboSwarmCoordAI on larger swarms and physical robotic platforms.