Abstract-- We consider reinforcement learning, and in particular, the Q-learning algorithm in large state and action spaces. In order to cope with the size of the spaces, a functio...
We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard docume...
Zi Yang, Keke Cai, Jie Tang, Li Zhang, Zhong Su, J...
We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a...
Gabriele Peters, Claus-Peter Alberts, Markus Bries...
We study how decentralized agents can develop a shared vocabulary without global coordination. Answering this question can help us understand the emergence of many communication s...
We show how to apply learning methods to two robotics problems, namely the optimization of the on-board controller of an omnidirectional robot, and the derivation of a model of the...