Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization

@inproceedings{Bakker2003HierarchicalRL,
  title={Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization},
  author={Bram Bakker and J{\"u}rgen Schmidhuber},
  year={2003}
}
We introduce a new method for hierarchical reinforcement learning. Highlevel policies automatically discover subgoals; low-level policies learn to specialize on different subgoals. Subgoals are represented as desired abstract observations which cluster raw input data. High-level value functions cover the state space at a coarse level; low-level value functions cover only parts of the state space at a fine-grained level. Experiments show that this method outperforms several flat reinforcement… CONTINUE READING
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Hierarchical reinforcement learning based on subgoal discovery and subpolicy specialization: First experiments with the HASSLE algorithm

  • B. Bakker, J. Schmidhuber
  • Technical report, IDSIA
  • 2003
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