View source for 17870212. TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES simplified abstract (Samsung Electronics Co., Ltd.)

=TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Jimmy Li of Longueuil (CA)|Jimmy Li of Longueuil (CA)]][[Category:Jimmy Li of Longueuil (CA)]]

[[:Category:Di Wu of Saint Laurent (CA)|Di Wu of Saint Laurent (CA)]][[Category:Di Wu of Saint Laurent (CA)]]

[[:Category:Yi Tian Xu of Montreal (CA)|Yi Tian Xu of Montreal (CA)]][[Category:Yi Tian Xu of Montreal (CA)]]

[[:Category:Tianyu Li of Montreal (CA)|Tianyu Li of Montreal (CA)]][[Category:Tianyu Li of Montreal (CA)]]

[[:Category:Seowoo Jang of Seoul (KR)|Seowoo Jang of Seoul (KR)]][[Category:Seowoo Jang of Seoul (KR)]]

[[:Category:Xue Liu of Montreal (CA)|Xue Liu of Montreal (CA)]][[Category:Xue Liu of Montreal (CA)]]

[[:Category:Gregory Lewis Dudek of Westmount (CA)|Gregory Lewis Dudek of Westmount (CA)]][[Category:Gregory Lewis Dudek of Westmount (CA)]]

==TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17870212 titled 'TRAFFIC SCENARIO CLUSTERING AND LOAD BALANCING WITH DISTILLED REINFORCEMENT LEARNING POLICIES

==Simplified Explanation==

The present disclosure describes a method, apparatus, and computer-readable storage media for load balancing traffic scenarios by a network device. 

* The method involves training multiple learning agents to load balance different traffic scenarios and obtain control policies.
* The method includes performing clustering iterations where pairs of control policies are merged into a clustered control policy.
* The clustering iterations continue until a predetermined number of control policies remain.
* The resulting control policies are then deployed to each base station in a network.

Potential applications of this technology include:

* Load balancing network traffic in telecommunications systems.
* Optimizing resource allocation in wireless networks.
* Improving network performance and efficiency.

Problems solved by this technology include:

* Uneven distribution of network traffic leading to congestion and poor performance.
* Inefficient allocation of network resources.
* Lack of adaptability to changing traffic patterns.

Benefits of this technology include:

* Improved network performance and reliability.
* Efficient utilization of network resources.
* Adaptability to changing traffic conditions.
* Reduced congestion and improved user experience.


==Original Abstract Submitted==

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.



[[Category:H04W28/08]]
[[Category:G06N20/00]]