A hierarchical approach to efficient reinforcement learning in deterministic domains

@inproceedings{Diuk2006AHA,
  title={A hierarchical approach to efficient reinforcement learning in deterministic domains},
  author={Carlos Diuk and Alexander L. Strehl and Michael L. Littman},
  booktitle={AAMAS},
  year={2006}
}
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-scale state spaces. We bring these three ideas together in a new algorithm. Our algorithm tackles two open problems from the reinforcement-learning literature, and provides a solution to those problems in deterministic domains. First, it shows how models can improve learning speed in the hierarchy-based MaxQ framework… CONTINUE READING
Highly Cited
This paper has 21 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

References

Publications referenced by this paper.
Showing 1-2 of 2 references

Reinforcement Learning: An Introduction

IEEE Transactions on Neural Networks • 1998
View 4 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…