Hierarchical Explanation-Based Reinforcement Learning

@inproceedings{Tadepalli1997HierarchicalER,
  title={Hierarchical Explanation-Based Reinforcement Learning},
  author={Prasad Tadepalli and Thomas G. Dietterich},
  booktitle={ICML},
  year={1997}
}
Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with the generalization ability of Explanation-Based Learning (EBL) (Di-etterich & Flann, 1995). We extend this work to domains where the agent must order and achieve a sequence of subgoals in an optimal fashion. Hierarchical EBRL can eeectively learn optimal policies in some of these sequential task domains even when the… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.
13 Citations
6 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-6 of 6 references

The effect of representation and knowledge on goal - directed exploration with reinforcement - learningalgorithms

  • R. G. Simmons
  • Machine Learning
  • 1996
2 Excerpts

Similar Papers

Loading similar papers…