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.