We invite you to submit papers that combine transfer learning with reinforcement learning. The deadline for submission is
February 7, 2017 February 14, 2017 (23:59 UTC −12:00), and decisions will be sent out on March 2, 2017 March 7, 2017 March 10, 2017.
Authors are encouraged to submit to any of the following categories:
Submissions should be in the AAMAS-17 format. The review process is double-blind and each submitted paper will be reviewed by at least two reviewers. Papers will be judged according to the chosen category, significance to the workshop, proposal quality, and clarity.
Reinforcement Learning (RL) has achieved many successes over the years in training autonomous agents to perform simple tasks. However, it takes a long time to learn a solution and this solution can usually only be applied to one specific task in a fixed setting.
Therefore, one of the major challenges in RL is to build intelligent agents that are able to transfer previously acquired knowledge to new tasks or transfer knowledge between agents in Multiagent RL systems. In this context, Transfer Learning (TL) describes an increasingly popular approach to accelerate learning by reusing and adapting knowledge.
Although there has already been some work on transfer for RL, this topic is gaining new interest through the rise of more sophisticated tools in RL which offer new possibilities. Currently there exists no general method which is able to learn autonomously what and how to transfer without additional background information about the task or the environment.
This workshop encourages the discussion of diverse approaches to accelerate and generalize RL with knowledge transfer. Our goal is to stimulate the investigation of different approaches to TL with the goal to get closer to a general unifying approach to TL in RL. Scaling-up RL methods with TL could have major implications on the research and practice on complex learning problems and will eventually lead to successful implementations in real-world applications.
We aim to bring together researchers working on different aspects to improve TL for RL with the goal to solve more complex problems more efficiently. We intend to make this an exciting event for researchers worldwide, not only for the presentation of top quality papers, but also to spark the discussion of opportunities and challenges for future research directions.
Examples of covered topics include (but are not limited to)
If you want to get in contact with the organization committee of the workshop please use the contact form provided here.