17957960. MULTI-BATCH REINFORCEMENT LEARNING VIA MULTI-IMITATION LEARNING simplified abstract (Samsung Electronics Co., Ltd.)
Contents
MULTI-BATCH REINFORCEMENT LEARNING VIA MULTI-IMITATION LEARNING
Organization Name
Inventor(s)
Michael Jenkin of Toronto (CA)
Gregory Lewis Dudek of Westmount (CA)
MULTI-BATCH REINFORCEMENT LEARNING VIA MULTI-IMITATION LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17957960 titled 'MULTI-BATCH REINFORCEMENT LEARNING VIA MULTI-IMITATION LEARNING
Simplified Explanation
The patent application describes a method for predicting future traffic load in base stations using artificial intelligence models. Here are the key points:
- The server receives traffic data from two base stations.
- It generates augmented traffic data for each base station by combining its own data with a subset of the other base station's data.
- It creates artificial intelligence models for each base station using imitation learning, based on the augmented traffic data.
- A generalized AI model is then created by distilling knowledge from the individual AI models.
- The generalized AI model is used to predict future traffic load for each base station.
Potential applications of this technology:
- Telecommunication networks: Predicting future traffic load can help optimize network resources and improve overall network performance.
- Traffic management: By accurately predicting traffic load, traffic management systems can better allocate resources and optimize traffic flow.
Problems solved by this technology:
- Accurate prediction: The use of artificial intelligence models and augmented data improves the accuracy of traffic load prediction.
- Resource optimization: By predicting future traffic load, network resources can be efficiently allocated, reducing congestion and improving performance.
Benefits of this technology:
- Improved network performance: By accurately predicting traffic load, network operators can optimize their infrastructure to handle the expected demand.
- Cost savings: Efficient resource allocation based on accurate predictions can lead to cost savings by avoiding unnecessary infrastructure upgrades.
- Enhanced user experience: Optimized network performance and reduced congestion result in a better user experience for customers.
Original Abstract Submitted
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.
