The abstract of the talk is provided below:

In Multi-access edge computing  based IoT networks, task scheduling requires balancing a critical trade-off between energy consumption and deadline violations. Standard reinforcement learning approaches are highly vulnerable to performance degradation when faced with unpredictable task arrival rates. To address this, we propose a novel provably robust reinforcement learning-based task allocation algorithm that maintains stable performance despite environmental volatility.