Conference proceeding
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on machine learning, Vol.227, pp.879-886
ICML '07
06/20/2007
Handle:
https://hdl.handle.net/2376/108941
Abstract
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer , a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.
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Details
- Title
- Cross-domain transfer for reinforcement learning
- Creators
- Matthew TaylorPeter Stone
- Publication Details
- Proceedings of the 24th international conference on machine learning, Vol.227, pp.879-886
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Series
- ICML '07
- Publisher
- ACM
- Identifiers
- 99900547099201842
- Language
- English
- Resource Type
- Conference proceeding