Samsung Electronics Co., Ltd.

Tianyu Li

Xue Liu

Gregory Lewis Dudek

Di Wu

Seowoo Jang

Michael Jenkin

Yi Tian Xu

David Meger

Jimmy Li

A server may receive a first traffic data and a second traffic data from a first base station and a second base station; obtain a first augmented traffic data for the first base station, based on the first traffic data and a subset data of the second traffic data; obtain a second augmented traffic data for the second base station, based on the second traffic data and a subset data of the first traffic data; obtain a first artificial intelligence (AI) model via imitation learning based on the first augmented traffic data; obtain a second AI model imitation learning based on the second augmented traffic data; obtain a generalized AI model via knowledge distillation from the first AI model and the second AI model; and predict a future traffic load of each of the first base station and the second base station based on the generalized AI model.

Multi-batch reinforcement learning via multi-imitation learning

The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.

Traffic scenario clustering and load balancing with distilled reinforcement learning policies

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Montreal, Canada

Tianyu Li