— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
In this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with glob...
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underli...