论文标题
具有有限维记忆的分散随机控制的平均场控制方法
Mean-Field Control Approach to Decentralized Stochastic Control with Finite-Dimensional Memories
论文作者
论文摘要
分散的随机控制（DSC）考虑了多代理系统的最佳控制问题。但是，除非在特殊情况下，除非在特殊情况下，因此无法解决DSC，因为药物之间的估计通常是棘手的。在这项工作中，我们提出了内存限制的DSC（ML-DSC），其中每个代理都会将观察记录压缩到有限维内存中。由于这种压缩简化了代理之间的估计，因此可以根据平均场控制理论在更一般的情况下解决ML-DSC。我们在一般LQG问题中演示了ML-DSC。由于估计和对照在一般的LQG问题中没有明确分开，因此Riccati方程将修改为分散的Riccati方程，从而改善了估计和对照。我们的数值实验表明，分散的riccati方程优于常规的riccati方程。
Decentralized stochastic control (DSC) considers the optimal control problem of a multi-agent system. However, DSC cannot be solved except in the special cases because the estimation among the agents is generally intractable. In this work, we propose memory-limited DSC (ML-DSC), in which each agent compresses the observation history into the finite-dimensional memory. Because this compression simplifies the estimation among the agents, ML-DSC can be solved in more general cases based on the mean-field control theory. We demonstrate ML-DSC in the general LQG problem. Because estimation and control are not clearly separated in the general LQG problem, the Riccati equation is modified to the decentralized Riccati equation, which improves estimation as well as control. Our numerical experiment shows that the decentralized Riccati equation is superior to the conventional Riccati equation.
