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@InProceedings{ALA13-MacAlpine,
  author = {Patrick MacAlpine and Elad Liebman and Peter Stone},
  title = {Simultaneous Learning and Reshaping of an Approximated Optimization Task},
  booktitle = {AAMAS Adaptive Learning Agents (ALA) Workshop},
  location = {Saint Paul, Minnesota, USA},
  month = {May},
  year = {2013},
  abstract={
For many target optimization and learning tasks the sample cost of performing 
the task is very expensive or time consuming such that attempting to directly 
employ a learning algorithm on the task becomes intractable.  For this reason 
learning is instead often performed on a less expensive task that is believed 
to be a reasonable approximation of the actual target task.  This paper serves 
to present and motivate the challenging open problem of simultaneously 
performing learning on an approximation of the true target task, while at the 
same time shaping the task used for learning to be a better representation of
the true target task.  Our work, which is still in progress, is performed in 
the RoboCup 3D simulation environment where we attempt to learn walk 
parameters for an omnidirectional walk engine used by humanoid robot soccer 
playing agents.  
  },
}