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Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces.
Learning hierarchical control structures for multiple tasks and changing environments
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Efficient adaptive-support association rule mining for recommender systems
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Complexity analysis of depth first and fp-growth implementations of apriori
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Chung-Cheng Chiu and Von-Wun Soo · 2011
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Automatic discovery and transfer of task hierarchies in reinforcement learning
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Seyed Sajad Mousavi, Behzad Ghazanfari, Nasser Mozayani, and Mohammad Reza Jahed-Motlagh · 2014
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Extracting bottlenecks for reinforcement learning agent by holonic concept clustering and attentional functions
Behzad Ghazanfari and Nasser Mozayani · 2016
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The option-critic architecture
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Michael Wynkoop and Thomas Dietterich · 2008
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Özgür Şimşek and Andrew G Barto · 2009
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Transfer in variable-reward hierarchical reinforcement learning
Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, and Alan Fern
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Automatic discovery and transfer of maxq hierarchies
Neville Mehta, Soumya Ray, Prasad Tadepalli, and Thomas Dietterich
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Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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