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Showing posts from July, 2007

Learning Symbolic Models

It is quite a common sense that successful generalization is the key to efficient learning in difficult environment. It appears to me that this must be especially true for reinforcement learning.
One potentially very powerful idea to achieve successful generalization is to learn symbolic models. Why? It is because a symbolic model (almost by definition) allows for very powerful generalizations (e.g. actions with parameters, state representation of environments with a variable number of objects with different object types, etc.).
JAIR just published the paper on this topic by H. M. Pasula, L. S. Zettlemoyer and L. P. Kaelbling, with the title "Learning Symbolic Models of Stochastic Domains". A brief glance reveals that the authors propose a greedy learning method, assuming a particular representation. The learning problem itself was shown earlier to be NP-hard, hence this sounds like a valid approach.
However, one thing is badly missing from this approach: learning the represen…