The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Probabilistic policy reuse in a reinforcement learning agent
Abstract
We contribute Policy Reuse as a technique to improve a reinforcement learning agent with guidance from past learned similar policies. Our method relies on using the past policies as a probabilistic bias where the learning agent faces three choices: the exploitation of the ongoing learned policy, the exploration of random unexplored actions, and the exploitation of past policies. We introduce the algorithm and its major components: an exploration strategy to include the new reuse bias, and a similarity function to estimate the similarity of past policies with respect to a new one. We provide empirical results demonstrating that Policy Reuse improves the learning performance over different strategies that learn without reuse. Interestingly and almost as a side effect, Policy Reuse also identifies classes of similar policies revealing a basis of core policies of the domain. We demonstrate that such a basis can be built incrementally, contributing the learning of the structure of a domain.
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Transfer Learning for Reinforcement Learning Domains: A Survey
Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,488
(2015) · Journal of Machine Learning Research · Cited by 1,119
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems
Silva, F. L. D., & Costa, A. H. R. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11396 · Cited by 273
Silver, T., Allen, K., Tenenbaum, J., & Kaelbling, L. (2018) · arXiv · doi:10.48550/arxiv.1812.06298 · Cited by 113
Metadata
- Authors
- Fernández, F., & Veloso, M.
- Venue
- Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
- Publisher
- ACM
- Published
- 2006-05-08
- Pages
- 720-727
- ISSN
- 1548-8403 (ISSN-L)
- OpenAlex
- W2031727428
- Cited by
- 275
- References
- 17
- FWCI
- 4.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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