In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …

Automated planning has been a continuous field of study since the 1960s, since the notion
of accomplishing a task using an ordered set of actions resonates with almost every known …

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic,
probabilistic environments. By interaction with the environment, reinforcement agents learn …

Reinforcement learning, and Q-learning in particular, encounter two major problems when
dealing with large state spaces. First, learning the Q-function in tabular form may be …

Reinforcement learning is a machine intelligence scheme for learning in highly dynamic and
probabilistic environments. The methodology, however, suffers from a major drawback; the …

Relational Reinforcement Learning: An Overview Page 1 Relational Reinforcement Learning:
An Overview Prasad Tadepalli tadepall@eecs.orst.edu School of Electrical Engineering and …

This book provides a unique treatment of an important area of machine learning and
answers the question of how kernel methods can be applied to structured data. Kernel …

We investigate the relation between transfer learning in reinforcement learning with function
approximation and supervised learning with concept drift. We present a new incremental …

Relational reinforcement learning is presented, a learning technique that combines
reinforcement learning with relational learning or inductive logic programming. Due to the …

Line primitives are a very powerful visual attribute used for scientific visualization and in
particular for 3D vector-field visualization. We extend the basic line primitives with additional …