CoDe：一种考虑能效的小规模无人机群协同与去中心化避障算法
CoDe: A Cooperative and Decentralized Collision Avoidance Algorithm for Small-Scale UAV Swarms Considering Energy Efficiency
AI总结：
本文提出CoDe算法，通过多智能体强化学习与新颖信用分配方案，解决小规模无人机群在连续动作空间下的协同避障与能效问题，实验证明其优于现有基准。
摘要：
本文提出了一种面向由最多三架无人机组成的小规模无人机群的协同与去中心化避障算法（CoDe）。CoDe通过实现无人机间的有效协作来提升无人机的能效。此外，CoDe专门针对无人机的运行进行了定制，解决了现有方案面临的挑战，如在连续动作空间中选择动作的无效性以及高计算复杂度。CoDe基于多智能体强化学习（MARL），并通过引入一种新颖的信用分配方案来寻找协同策略。该新颖的信用分配方案通过从集群的联合动作值中减去一个基线来估计个体的贡献。CoDe中的信用分配方案优于其他基准，因为该基线不仅考虑了无人机动作的重要性，还考虑了无人机之间的相互关系。此外，还针对现有的基于MARL的算法和传统的基于启发式的算法进行了大量实验，以证明所提算法的优势。
英文摘要：
This paper introduces a cooperative and decentralized collision avoidance algorithm (CoDe) for small-scale UAV swarms consisting of up to three UAVs. CoDe improves energy efficiency of UAVs by achieving effective cooperation among UAVs. Moreover, CoDe is specifically tailored for UAV's operations by addressing the challenges faced by existing schemes, such as ineffectiveness in selecting actions from continuous action spaces and high computational complexity. CoDe is based on Multi-Agent Reinforcement Learning (MARL), and finds cooperative policies by incorporating a novel credit assignment scheme. The novel credit assignment scheme estimates the contribution of an individual by subtracting a baseline from the joint action value for the swarm. The credit assignment scheme in CoDe outperforms other benchmarks as the baseline takes into account not only the importance of a UAV's action but also the interrelation between UAVs. Furthermore, extensive experiments are conducted against existing MARL-based and conventional heuristic-based algorithms to demonstrate the advantages of the proposed algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
