Recently researchers have introduced methods to develop reusable knowledge in reinforcement learning (RL). In this paper, we define simple principles to combine skills in reinforce...
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
Web-Streaming lectures overcome the space and time barriers between learning and teaching, but bring higher requirements on the learning feedback of students when they browse lectu...
The rapid evolution and ubiquitous use of mobile devices is an historical opportunity to improve experiential interactivity in education practices to support “deep” learning. ...
Andrew Litchfield, Ryszard Raban, Laurel Evelyn Dy...
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a mor...