We describe a novel framework developed for transfer learning within reinforcement learning (RL) problems. Then we exhibit how this framework can be extended to intelligent tutorin...
Kimberly Ferguson, Beverly Park Woolf, Sridhar Mah...
We describe a system that successfully transfers value function knowledge across multiple subdomains of realtime strategy games in the context of multiagent reinforcement learning....
We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous w...
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...
To gain insights into the neural basis of such adaptive decision-making processes, we investigated the nature of learning process in humans playing a competitive game with binary ...