Abstract. We consider the problem of learning an acyclic discrete circuit with n wires, fan-in bounded by k and alphabet size s using value injection queries. For the class of tran...
Dana Angluin, James Aspnes, Jiang Chen, Lev Reyzin
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced tasks to speed up learning on a new task. So far, rese...
The paper presents LOCO-Analyst, an educational tool for providing teachers with feedback on the relevant aspects of the learning process taking place in a web-based learning envir...
Jelena Jovanovic, Dragan Gasevic, Christopher A. B...
We study the properties of the agnostic learning framework of Haussler [Hau92] and Kearns, Schapire and Sellie [KSS94]. In particular, we address the question: is there any situat...