Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...
Finding good representations of text documents is crucial in information retrieval and classification systems. Today the most popular document representation is based on a vector ...
Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each ...
Abstract. This paper presents a methodology for automatically customizing a scenario to suit a learner’s abilities, needs, or goals. Training scenarios are often utilized to give...
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...