Speeding up Learning: Action Video Games and Perceptual Learning

Learning, Attentional Control, and Action Video Games

While humans have an incredible capacity to acquire new skills and alter their behavior as a result of experience, enhancements in performance are typically narrowly restricted to the parameters of the training environment, with little evidence of generalization to different, even seemingly highly related, tasks. Such specificity is a major obstacle for the development of many real-world traini...

Perceptual Learning During Action Video Game Playing

Action video games have been shown to enhance behavioral performance on a wide variety of perceptual tasks, from those that require effective allocation of attentional resources across the visual scene, to those that demand the successful identification of fleetingly presented stimuli. Importantly, these effects have not only been shown in expert action video game players, but a causative link ...

Speeding up Tabular Reinforcement Learning Using State-Action Similarities

One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These handcoded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach’s benefits.

Speeding-up ProbLog's Parameter Learning

ProbLog is a state-of-art combination of logic programming and probabilities; in particular ProbLog offers parameter learning through a variant of the EM algorithm. However, the resulting learning algorithm is rather slow, even when the data are complete. In this short paper we offer some insights that lead to orders of magnitude improvements in ProbLog’s parameter learning speed with complete ...

Speeding-up Reinforcement Learning with