1 Reinforcement learning has become a widely used methodology for creating intelligent agents in a wide range of applications. However, its performance deteriorates in tasks with s...
Abstract. Research on practical models of autonomous agents has largely focused on a procedural view of goal achievement. This allows for efficient implementations, but prevents an...
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 an autonomic machine control system applied to the adaptive control of a modular soldering machine. The particular case concerns the creation of a novel modul...
Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling si...