Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
Performance measurement systems based on the principle that "if you can't measure it, you can't manage it" reinforce a short-term culture by focussing on tangi...
In this paper we present VOCUS: a robust computational attention system for goal-directed search. A standard bottom-up architecture is extended by a top-down component, enabling th...
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that...