Competitive Learning for Autonomous Flight

Abstract

The competition among a swarm often brings about astonishing potential beyond the ordinary. This paper proposes an approach for enhancing autonomous obstacle avoidance in aerial robots through competitive training. By constructing a multi-agent environment, we introduce competition between individuals as a form of comparative learning, stimulating more robust end-to-end flight strategies. Unlike traditional single-agent reinforcement learning (RL) methods, our framework leverages competitive interactions among virtual agents to generate richer training signals, leading to superior performance. Experimental results demonstrate that policies trained in this competitive multi-agent setting outperform those derived from single-drone simulation-based RL, achieving higher efficiency and adaptability in complex environments. Furthermore, ablation studies are conducted to validate the effectiveness and limitations of competitive learning. This work highlights the potential of competitive learning paradigms in advancing autonomous aerial robot navigation.