Using a multi-agent transformer where each TSN queue is an agent cuts simulated XR deadline misses and queueing latency compared to PPO, A2C, HAPPO, and backlog or deadline heuristics.
Edge learning via federated split decision transformers for metaverse resource allocation,
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Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks
Using a multi-agent transformer where each TSN queue is an agent cuts simulated XR deadline misses and queueing latency compared to PPO, A2C, HAPPO, and backlog or deadline heuristics.