A digital twin-based INC-MEC framework models XR user interactions as a Stackelberg Markov game and uses a Nash-asynchronous hybrid multi-agent RL algorithm to achieve Nash Equilibrium, improving system utility, uplink rate, and energy efficiency in simulations.
Reinforcement learning for joint optimization of communication and computation in vehicular networks
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Digital Twin-Assisted In-Network and Edge Collaboration for Joint User Association, Task Offloading, and Resource Allocation in the Metaverse
A digital twin-based INC-MEC framework models XR user interactions as a Stackelberg Markov game and uses a Nash-asynchronous hybrid multi-agent RL algorithm to achieve Nash Equilibrium, improving system utility, uplink rate, and energy efficiency in simulations.