A reinforcement-learning approach to power management

We describe an adaptive, mid-level approach to the wireless device power manage-ment problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad hoc wireless networks. From this thesis we conclude that mid-level power management policies can outper-form low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements