In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
This paper describes Steve, an animated agent that helps students learn to perform physical, procedural tasks. The student and Steve cohabit a three-dimensional, simulated mock-up...
How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data se...
In this paper we investigate the role of reflection in simulation based learning by manipulating two independent factors that each separately lead to significant learning effects, ...
In concurrent cooperative multiagent learning, each agent simultaneously learns to improve the overall performance of the team, with no direct control over the actions chosen by i...