Learning-Based Control for a Communicating Mobile Robot under Unknown Rates

Abstract

In problems such as surveying or monitoring remote regions, a mobile robot must transmit data over a wireless network with unknown, position-dependent transmission rates. We propose an algorithm to achieve this objective that learns approximations of the rate function and of an optimal-control solution that transmits the data in minimum time. The rates are estimated with supervised learning from the samples observed; and the control is found with dynamic programming sweeps around the current state of the robot that exploit the rate function estimate, combined with online reinforcement learning. For both synthetic and realistic rate functions, our experiments show that the learning algorithm empties the data buffer in less than twice the number of steps achieved by a model-based solution that requires to perfectly know the rate function.