Real-Time Scheduling via Reinforcement Learning
This addresses the challenge of adaptive scheduling in dynamic environments for applications such as robotics, but it is incremental as it builds on existing reinforcement learning approaches.
The paper tackles the problem of real-time scheduling for cyber-physical systems like mobile robots, where tasks must be balanced adaptively without prior knowledge, and demonstrates that efficient learning is possible by leveraging problem structure despite an infinite state space.
Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the need to perform more general tasks such as obstacle avoidance. This problem has been addressed by maintaining relative utilization of shared resources among tasks near a user-specified target level. Producing optimal scheduling strategies requires complete prior knowledge of task behavior, which is unlikely to be available in practice. Instead, suitable scheduling strategies must be learned online through interaction with the system. We consider the sample complexity of reinforcement learning in this domain, and demonstrate that while the problem state space is countably infinite, we may leverage the problem's structure to guarantee efficient learning.