NETWORK OPTIMIZATION BASED ON DISTRIBUTED MULTI-AGENT MACHINE LEARNING WITH MINIMAL INTER-AGENT DEPENDENCY

Abstract

Network optimization based on distributed multi-agent machine learning with minimal inter-agent dependency is disclosed. At least some of the embodiments may allow a distributed multi-agent deep reinforcement learning (DRL) algorithm for a mobility robustness optimization (MRO) problem, where each agent may comprise a varying number of physical or logical network boundaries. At least some of the embodiments may allow minimizing inter-agent dependencies by decomposing a network mobility graph. At least some of the embodiments may allow a transfer learning framework for self-organizing network (SON) model profiling, storage, retrieval, retraining, and management such that one can efficiently retrieve a SON model that was pre-trained in a similar (sub)network environment.

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