论文标题
探索性LQG平均野外游戏带有熵正则化
Exploratory LQG Mean Field Games with Entropy Regularization
论文作者
论文摘要
我们与$ k $不同的代理人的子人群不断地研究了一类通用的熵登记的多变量LQG平均野外游戏（MFGS）。我们将动作的概念扩展到动作分布（探索性动作），并明确地在限制MFG中为各个代理提供了最佳动作分布。我们证明，最佳的动作分布集为有限的熵侦查MFG产生$ε$ -NASH平衡。此外，我们将最终的解决方案与经典LQG MFG的解决方案进行了比较，并确定其存在的等效性。
We study a general class of entropy-regularized multi-variate LQG mean field games (MFGs) in continuous time with $K$ distinct sub-population of agents. We extend the notion of actions to action distributions (exploratory actions), and explicitly derive the optimal action distributions for individual agents in the limiting MFG. We demonstrate that the optimal set of action distributions yields an $ε$-Nash equilibrium for the finite-population entropy-regularized MFG. Furthermore, we compare the resulting solutions with those of classical LQG MFGs and establish the equivalence of their existence.
