Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
—In this paper we present a new approach called cue-based networking that uses hints or cues about the physical environment to optimize networked application behavior. We define...
— In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to pre...
Axel Rottmann, Christian Plagemann, Peter Hilgers,...
We investigate resource allocation policies for time-division multiple access (TDMA) over fading channels in the power-limited regime. For frequency-flat block-fading channels and ...
We evaluate two systems for automatically generating personalized interfaces adapted to the individual motor capabilities of users with motor impairments. The first system, SUPPLE...
Krzysztof Z. Gajos, Jacob O. Wobbrock, Daniel S. W...