Feature Reinforcement Learning Using Looping Suffix Trees
European Workshop on Reinforcement Learning (EWRL)（2012）
Australian National University Research School of Computer Science
Abstract
There has recently been much interest in history-based methods using suffix trees to solve POMDPs. However, these suffix trees cannot efficiently represent environments that have long-term dependencies. We extend the recently introduced CTMDP algorithm to the space of looping suffix trees which have previously only been used in solving determinis- tic POMDPs. The resulting algorithm replicates results from CTMDP for environments with short term dependencies, while it outperforms LSTM-based methods on TMaze, a deep memory environment.
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Behavior Trees,Approximate Matching
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