Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines
Abstract
Existing reinforcement learning approaches are often hampered by learning tabula rasa . Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned results, but may require an inter-task mapping to encode how the previously learned task and the new task are related. This paper presents an autonomous framework for learning inter-task mappings based on an adaptation of restricted Boltzmann machines. Both a full model and a computationally efficient factored model are introduced and shown to be effective in multiple transfer learning scenarios.
Cite
Text
Bou-Ammar et al. "Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40991-2_29Markdown
[Bou-Ammar et al. "Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/bouammar2013ecmlpkdd-automatically/) doi:10.1007/978-3-642-40991-2_29BibTeX
@inproceedings{bouammar2013ecmlpkdd-automatically,
title = {{Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines}},
author = {Bou-Ammar, Haitham and Mocanu, Decebal Constantin and Taylor, Matthew E. and Driessens, Kurt and Tuyls, Karl and Weiss, Gerhard},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {449-464},
doi = {10.1007/978-3-642-40991-2_29},
url = {https://mlanthology.org/ecmlpkdd/2013/bouammar2013ecmlpkdd-automatically/}
}