Interview with Tianfu Wang: A reinforcement learning framework for network resource allocation

In their work FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation, accepted at IJCAI 2024, Tianfu Wang, Qilin Fan, Chao Wang, Long Yang, Leilei Ding, Nicholas Jing Yuan and Hui Xiong introduce a framework for addressing resource allocation problems. In this interview, Tianfu Wang tells us more about their framework, the implications of their research, and what they are planning next. Our paper focuses on addressing resource allocation problems using a reinforcement learning (RL) framework, specifically in the domain of network virtualization, known as virtual network embedding (VNE). VNE involves efficiently mapping virtual network requests onto physical infrastructure. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability.