While AI Planning and Reinforcement Learning communities focus on similar sequential decision-making problems, these communities remain somewhat unaware of each other on specific problems, techniques, methodologies, and evaluation.
This workshop aims to encourage discussion and collaboration between the researchers in the fields of AI planning and reinforcement learning. We aim to bridge the gap between the two communities, facilitate the discussion of differences and similarities in existing techniques, and encourage collaboration across the fields. We solicit interest from AI researchers that work in the intersection of planning and reinforcement learning, in particular, those that focus on intelligent decision making. As such, the joint workshop program is an excellent opportunity to gather a large and diverse group of interested researchers.
The workshop solicits work at the intersection of the fields of reinforcement learning and planning. We also solicit work solely in one area that can influence advances in the other so long as the connections are clearly articulated in the submission.
Submissions are invited for topics on, but not limited to:
- Reinforcement learning (model-based, Bayesian, deep, etc.)
- Model representation and learning for planning
- Planning using approximated/uncertain (learned) models
- Monte Carlo planning
- Learning search heuristics for planner guidance
- Theoretical aspects of planning and reinforcement learning
- Reinforcement Learning and planning competition(s)
- Multi-agent planning and learning
- Applications of both reinforcement learning and planning
Important Dates (UPDATED on March 30, 2020)
- Submission deadline: August 3rd, 2020 (UTC-12 timezone)
- Notification date: August 26th, 2020
- Camera-ready deadline: September 25th, 2020
- Workshop date: October 26th or 27th (TBD), 2020
We solicit workshop paper submissions relevant to the above call of the following types:
- Long papers — up to 8 pages + unlimited references / appendices
- Short papers — up to 4 pages + unlimited references / appendices
- Extended abstracts — up to 2 pages + unlimited references / appendices
Please format submissions in AAAI style (see instructions in the Author Kit at AAAI, AuthorKit20.zip) and keep them to at most 9 pages including references. Authors considering submitting to the workshop papers rejected from other conferences, please ensure you do your utmost to address the comments given by the reviewers. Please do not submit papers that are already accepted for the main ICAPS conference to the workshop.
Some accepted long papers will be accepted as contributed talks. All accepted long and short papers and extended abstracts will be given a slot in the poster presentation session. Extended abstracts are intended as brief summaries of already published papers, preliminary work, position papers or challenges that might help bridge the gap.
As the main purpose of this workshop is to solicit discussion, the authors are invited to use the appendix of their submissions for that purpose.
Paper submissions should be made through EasyChair, https://easychair.org/conferences/?conf=prl2020.
- Chair – Michael Katz, IBM T.J. Watson Research Center, NY, USA
- Chair – Hector Palacios, Element AI, Montreal, Canada
- Alan Fern, Oregon State University, USA
- Vicenç Gómez, Universitat Pompeu Fabra, Barcelona, Spain
- Anders Jonsson, Universitat Pompeu Fabra, Barcelona, Spain
- Scott Sanner, University of Toronto, Toronto, Canada
Please send your inquiries by email to the organizers.