基于多智能体的动态邻域优化任务分配
Dynamic neighbourhood optimisation for task allocation using multi-agent
摘要：
在大规模系统中，当集中式技术用于任务分配时，存在根本性的挑战。交互的数量受到诸如计算、存储和网络通信等资源约束的限制。我们可以通过将系统实现为分布式任务分配系统来提高可扩展性，在许多代理之间共享任务。然而，这也增加了通信和同步的资源成本，并且难以扩展。在本文中，我们提出了四个算法来解决这些问题。这些算法的组合使每个代理能够通过强化学习来改进他们的任务分配策略，同时根据他们过去的经验，根据他们认为当前策略的最佳程度来改变他们探索系统的程度。我们专注于分布式代理系统，其中代理的行为受到资源使用限制的约束，将代理限制为本地知识而不是系统范围的知识。我们评估这些算法在一个模拟的环境中，代理人被赋予的任务组成的多个子任务，必须分配给其他代理具有不同的能力，然后执行这些任务。我们还模拟了现实生活中的系统影响，如网络不稳定性。我们的解决方案是解决任务分配问题的6.7%的理论最优的系统配置内考虑。当系统连接受到影响时，它提供了比无知识保留方法高5倍的性能恢复，并且针对多达100个代理的系统进行了测试，对算法性能的影响小于9%。
英文摘要：
In large-scale systems there are fundamental challenges when centralised techniques are used for task allocation. The number of interactions is limited by resource constraints such as on computation, storage, and network communication. We can increase scalability by implementing the system as a distributed task-allocation system, sharing tasks across many agents. However, this also increases the resource cost of communications and synchronisation, and is difficult to scale. In this paper we present four algorithms to solve these problems. The combination of these algorithms enable each agent to improve their task allocation strategy through reinforcement learning, while changing how much they explore the system in response to how optimal they believe their current strategy is, given their past experience. We focus on distributed agent systems where the agents' behaviours are constrained by resource usage limits, limiting agents to local rather than system-wide knowledge. We evaluate these algorithms in a simulated environment where agents are given a task composed of multiple subtasks that must be allocated to other agents with differing capabilities, to then carry out those tasks. We also simulate real-life system effects such as networking instability. Our solution is shown to solve the task allocation problem to 6.7% of the theoretical optimal within the system configurations considered. It provides 5x better performance recovery over no-knowledge retention approaches when system connectivity is impacted, and is tested against systems up to 100 agents with less than a 9% impact on the algorithms' performance.
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。
