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Graph-Based Domain Mapping for Transfer Learning in General Games.
Gregory
Kuhlmann and Peter Stone.
In Proceedings of The Eighteenth European
Conference on Machine Learning, September 2007.
[PDF]377.0kB [postscript]601.6kB
A general game player is an agent capable of taking as input a description of a game's rules in a formal language and proceeding to play without any subsequent human input. To do well, an agent should learn from experience with past games and transfer the learned knowledge to new problems. We introduce a graph-based method for identifying previously encountered games and prove its robustness formally. We then describe how the same basic approach can be used to identify similar but non-identical games. We apply this technique to automate domain mapping for value function transfer and speed up reinforcement learning on variants of previously played games. Our approach is fully implemented with empirical results in the general game playing system.
@Inproceedings(ECML07-rulegraphs,
author="Gregory Kuhlmann and Peter Stone",
title="Graph-Based Domain Mapping for Transfer Learning in General Games",
booktitle="Proceedings of The Eighteenth European Conference on Machine Learning",
month="September",
year="2007",
abstract={A general game player is an agent capable of taking as
input a description of a game's rules in a formal language and
proceeding to play without any subsequent human input. To do
well, an agent should learn from experience with past games and
transfer the learned knowledge to new problems. We introduce a
graph-based method for identifying previously encountered games
and prove its robustness formally. We then describe how the same
basic approach can be used to identify similar but non-identical
games. We apply this technique to automate domain mapping for
value function transfer and speed up reinforcement learning on
variants of previously played games. Our approach is fully
implemented with empirical results in the general game playing
system. },
)
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