Hierarchical Explanation-Based Reinforcement Learning (1997)
| Venue: | In Proceedings of the Fourteenth International Conference on Machine Learning |
| Citations: | 15 - 4 self |
BibTeX
@INPROCEEDINGS{Tadepalli97hierarchicalexplanation-based,
author = {Prasad Tadepalli and Thomas G. Dietterich},
title = {Hierarchical Explanation-Based Reinforcement Learning},
booktitle = {In Proceedings of the Fourteenth International Conference on Machine Learning},
year = {1997},
pages = {358--366},
publisher = {Morgan Kaufmann}
}
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Abstract
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) (Dietterich & 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 effectively learn optimal policies in some of these sequential task domains even when the subgoals weakly interact with each other. We also show that when a planner that can achieve the individual subgoals is available, our method converges even faster. 1 Introduction Reinforcement Learning (RL) has emerged as the method of choice for building autonomous agents that improve their performance with experience. One obstacle to scaling this approach to large problems is the lack of a robust and justifiable method to generalize from one experience to another. Dietterich and Flann (Dietter...
Citations
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| 42 | Explanation-based learning and reinforcement learning: A unified view - Dietterich, Flann - 1995 |
| 39 | Acquiring recursive and iterative concepts with explanation-based learning - Shavlik - 1990 |
| 28 | Model-based average reward reinforcement learning - Tadepalli, Ok - 1998 |
