Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
This paper presents an investigation into exploiting the population-based nature of Learning Classifier Systems for their use within highly-parallel systems. In particular, the use...
Larry Bull, Matthew Studley, Anthony J. Bagnall, I...
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Transfer learning problems are typically framed as leveraging knowledge learned on a source task to improve learning on a related, but different, target task. Current transfer met...
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...