AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 10 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work proposes a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC and employs multi-agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions.
This paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent and the Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery a...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.