In this study, a partially observed linear quadratic (LQ) non-cooperative stochastic differential game of Markov jump linear system (MJLS) is considered. First, a new proof of filtering theory is given, using the separation principle and filtering technology, partial observations can be transformed into complete observation. Second, nonzero-sum stochastic differential game problem for MJLS are studied using the dynamic programming principle and completion square method.Then, the sufficient and necessary conditions for the Nash equilibrium are obtained. Finally, a numerical simulation is performed.

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In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

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