Abstract
Optimal inventory upgrade planning is one of the most challenging tasks in managing network assets. A Smart Network Inventory Planning (SNIP) architecture or framework is presented herein that leverages a deep reinforcement learning (DRL) framework to enable network inventory upgrade planning for different scenarios. As a foundation for the DRL framework, techniques herein provide for establishing a network inventory environment through which interaction with a supply chain can be used to allow the SNIP architecture to incrementally optimize upgrading sequences for multiple customers. To further optimize inventory upgrades via the DRL framework, the SNIP architecture may employ a multi-objective reward function. Additionally, a transformer can be utilized as a policy network to capture long-term correlations in the inventory upgrading sequence. By incorporating weighting coefficients into both the reward function and a multi-agent actor network, the SNIP architecture can provide customized inventory task scheduling within an optimal framework.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Sun, Pengfei; Shao, Qihong; and Singh, Gurvinder, "CROSS CUSTOMERS SMART NETWORK INVENTORY PLANNER (SNIP) AND OPTIMIZATIONS USING DEEP REINFORCEMENT LEARNING", Technical Disclosure Commons, (October 16, 2023)
https://www.tdcommons.org/dpubs_series/6321