The theme of this year's workshop is Planning in Reinforcement Learning. Planning means any way of using computation to go from a model of the world to a good policy or value function. A model of the world is any way of going from a state and a proposed course of action to a next state (and reward along the way). We are particularly interested in models that could be learned rather than ones that have to be provided by people. Good subtopics for inclusion in this year's workshop 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 echos of these themes in this year's meeting. |