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.
翻译：分散式随机控制(DSC) 考虑多试剂系统的最佳控制问题。 但是, DSC 只有在特殊情况下才能解决,因为代理商之间的估计通常难以解决。 在这项工作中,我们建议每个代理商将观测历史压缩到有限维内存中,每个代理商都采用内存限制 DSC(ML-DSC ) 。由于这种压缩简化简化了代理商之间的估计, ML-DSC 可以在基于平均场控制理论的更一般情况下得到解决。 我们在一般LQG问题中展示了 ML-DSC 。由于一般LQG 问题的估算和控制没有明确分开,Riccati 方程式被修改为分散式的Riccati方程式,这样可以改进估计和控制。我们的数字实验表明,分散式Riccati方程式比传统的Riccati方程式要优越。
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