多智能体系统集中控制中任务有效的观测压缩
Task-Effective Compression of Observations for the Centralized Control of a Multi-agent System Over Bit-Budgeted Channels
摘要：
我们考虑一个任务有效的量化问题，当多个代理人通过一个集中式控制器（CC）的控制。虽然代理必须将其观察传达给CC以用于决策，但是代理-CC链路的比特预算通信可能限制系统的任务有效性，该任务有效性由系统的阶段成本/回报的平均总和来测量。因此，每个智能体应该压缩/压缩其观察结果，使得控制任务的阶段成本/奖励的平均总和受到最小的影响。我们解决的问题，最大化的阶段奖励的平均总和，提出了两种不同的基于状态聚合（ABSA）算法进行间接和联合设计的控制和通信策略的多智能体系统。虽然ABSA-1的适用性仅限于单代理系统，但它提供了一个分析框架，作为ABSA-2设计的垫脚石。ABSA-2实现了多智能体系统的控制与通信的联合设计。我们评估的算法-平均回报作为性能指标-使用数值实验进行解决多代理几何共识问题。数值结果的结论是通过引入一个新的度量标准，衡量在多智能体系统中的通信的有效性。
英文摘要：
We consider a task-effective quantization problem that arises when multiple agents are controlled via a centralized controller (CC). While agents have to communicate their observations to the CC for decision-making, the bit-budgeted communications of agent-CC links may limit the task-effectiveness of the system which is measured by the system's average sum of stage costs/rewards. As a result, each agent should compress/quantize its observation such that the average sum of stage costs/rewards of the control task is minimally impacted. We address the problem of maximizing the average sum of stage rewards by proposing two different Action-Based State Aggregation (ABSA) algorithms that carry out the indirect and joint design of control and communication policies in the multi-agent system. While the applicability of ABSA-1 is limited to single-agent systems, it provides an analytical framework that acts as a stepping stone to the design of ABSA-2. ABSA-2 carries out the joint design of control and communication for a multi-agent system. We evaluate the algorithms - with average return as the performance metric - using numerical experiments performed to solve a multi-agent geometric consensus problem. The numerical results are concluded by introducing a new metric that measures the effectiveness of communications in a multi-agent system.
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