Stable Reinforcement Learning with Unbounded State Space

Understand

We consider the problem of reinforcement learning (RL) with unbounded state space motivated by the classical problem of scheduling in a queueing network.

• Traditional policies as well as error metric that are designed for finite, bounded or compact state space, require infinite samples for providing any meaningful performance guarantee (e.g.
• $\ell_\infty$ error) for unbounded state space.
• That is, we need a new notion of performance metric.