State Abstraction in MAXQ Hierarchical Reinforcement Learning


Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstractions, in which abstract actions are defined that can perform many primitive actions before terminating. However, little is known about learning with state abstractions, in which aspects of the state space are ignored. In previous work, we developed the… (More)


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@inproceedings{Dietterich1999StateAI, title={State Abstraction in MAXQ Hierarchical Reinforcement Learning}, author={Thomas G. Dietterich}, booktitle={NIPS}, year={1999} }