We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
A new hierarchical nonparametric Bayesian framework is proposed for the problem of multi-task learning (MTL) with sequential data. The models for multiple tasks, each characterize...
Kai Ni, John William Paisley, Lawrence Carin, Davi...
Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots. In th...
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, lar...