We present a novel technique for automated problem decomposition to address the problem of scalability in reinforcement learning. Our technique makes use of a set of near-optimal ...
Peng Zang, Peng Zhou, David Minnen, Charles Lee Is...
Relativized options combine model minimization methods and a hierarchical reinforcement learning framework to derive compact reduced representations of a related family of tasks. ...
Software development is knowledge-intensive as well as collaborative work carried out by several persons. In this type of education, project-based exercises are conducted in order ...
In this paper we present a new method, time-striding hidden Markov model (TSHMM), to learn from long-term motion for atomic behaviors and the statistical dependencies among them. T...
Guided by the cooperation theory, this paper puts forward an interactive and cooperative learning environment design that is based on the self-learning mode and cooperative learnin...