Joint workshop at ICML, UAI, and COLT 2009
 June 18, 2009, Montreal, Canada

Call for Papers

Accepted Papers

Program Committee

Submission Instructions

Important Dates



Photo Album

ICML Workshop Video Lectures

Call for Papers

We welcome submissions on all aspects of abstraction in Reinforcement Learning, including, but not limited to, papers addressing the following topics:

  • Representation   Novel representational frameworks for temporal and state abstraction; ex- periences with existing frameworks.
  • Discovery   Methods that allow artificial agents to perform state and temporal abstraction autonomously, using their experience in their environment.
  • Algorithms   Learning and planning algorithms that can fully take advantage of temporal abstraction by reasoning at the correct temporal granularity in the presence of actions at different time scales.
  • Applications   Descriptions of real-world applications that make effective use of various abstraction methods; suggestions for real-world and simulated environments that can support ongoing research in the area.
  • Synergy   Methods that use one type of abstraction to discover or improve the performance in another type of abstraction.
  • Overview/Methodology   Reviews of existing methods; comments on methodology; new research directions.

Submissions will be reviewed by program committee members on the basis of relevance, significance, technical quality, and clarity.