Using Localization and Factorization to Reduce the Complexity of Reinforcement Learning
General reinforcement learning is a powerful framework for artificial intelligence that has seen much theoretical progress since introduced fifteen years ago. We have previously provided guarantees for cases with finitely many possible environments. Though the results are the best possible in general, a linear dependence on the size of the hypothesis class renders them impractical. However, we dramatically improved on these by introducing the concept of environments generated by combining laws....[Show more]
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|Source:||Journal of Artificial General Intelligence|
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