Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

Seongin Na, Tomás Roucek, Jirí Ulrich, Jan Pikman, Tomás Krajník, Barry Lennox, Farshad Arvin

Abstract: The recent advancement of deep reinforcement learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex controllers than a single robot system to lead a desired collective behavior. Although the DRL-based controller design method showed its effectiveness for swarm robotic systems, the reliance on the central training server is a critical problem in real-world environments where robot-server communication is unstable or limited. We propose a novel federated learning (FL)-based DRL training strategy federated learning DDPG (FLDDPG) for use in swarm robotic applications. Through the comparison with baseline strategies under a limited communication bandwidth scenario, it is shown that the FLDDPG method resulted in higher robustness and generalization ability into a different environment and real robots, while the baseline strategies suffer from the limitation of communication bandwidth. This result suggests that the proposed method can benefit swarm robotic systems operating in environments with limited communication bandwidth, e.g., in high radiation, underwater, or subterranean environments.