897 citations · 2.1k across the 32 of their papers we have counts for
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cs.LG1999★ 54 cited
State Abstraction in MAXQ Hierarchical Reinforcement Learning
Thomas G. Dietterich
Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstract actions are defined that can perform many primitiv…
cs.LG1999★ 21 cited
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Thomas G. Dietterich
This paper presents the MAXQ approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and deco…