The theme of this year's workshop is Putting it all together: Integrated Architectures for Reinforcement Learning.
Much work in reinforcement learning is focused on specific algorithms addressing individual issues (e.g., exploration, function approximation, planning, etc). In this workshop, we want to explore ways of pulling together multiple different aspects of RL for solving AI or understanding natural intelligence. We are interested in algorithms that address more than just a single aspect of RL in isolation, and which speak to our ability to build AI architectures in which RL is an important component.
Potentially relevant sub-topics include (but are not limited to):
Finally, although it is good to have a theme each year, there is always residual interest in previous year's themes. Some themes from past years that seem to keep recurring are life-long learning, perceptual learning and representational change, state estimation, function approximation, real-time learning, and temporal abstraction. It would not be inappropriate for there to be echoes of these themes in this year's meeting.