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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.
The multi-agent transformer (MAT) is adopted to model inter-queue dependencies via attention over agents' observations and actions, enabling implicit coordination across heterogeneous co-located XR applications and results show that the proposed method outperforms baselines.
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...
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