Search Machine Learning Repository: @inproceedings{icml2014c2_scholz14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {A Physics-Based Model Prior for Object-Oriented MDPs},
    Url = {http://jmlr.org/proceedings/papers/v32/scholz14.pdf},
    Abstract = {One of the key challenges in using reinforcement learning in robotics is the need for models that capture natural world structure. There are, methods that formalize multi-object dynamics using relational representations, but these methods are not sufficiently compact for real-world robotics. We present a physics-based approach that exploits modern simulation tools to efficiently parameterize physical dynamics. Our results show that this representation can result in much faster learning, by virtue of its strong but appropriate inductive bias in physical environments.},
    Author = {Jonathan Scholz and Martin Levihn and Charles Isbell and David Wingate},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1089-1097}
   }