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.