The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Identifying useful subgoals in reinforcement learning by local graph partitioning
Open accessAbstract
We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs---those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discovery method allows it to successfully identify the type of subgoals we seek---states that lie between two densely-connected regions of the state space while producing an algorithm with low computational cost.
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Cites
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L.-J. (1992) · Machine Learning · doi:10.1007/bf00992699 · Cited by 1,564
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The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4650 · Cited by 1,000
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Kulkarni, T. D., Narasimhan, K., Saeedi, A., & Tenenbaum, J. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06057 · Cited by 770
Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective
Botvinick, M. M., Niv, Y., & Barto, A. G. (2009) · Cognition · doi:10.1016/j.cognition.2008.08.011 · Cited by 646
Self-citationProbabilistic policy reuse in a reinforcement learning agent
Fernández, F., & Veloso, M. (2006) · Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1160633.1160762 · Cited by 275
Transfer in Reinforcement Learning: A Framework and a Survey
Lazaric, A. (2012) · Adaptation, Learning, and Optimization · doi:10.1007/978-3-642-27645-3_5 · Cited by 256
Metadata
- Authors
- Şimşek, Ö., Wolfe, A. P., & Barto, A. G.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 816-823
- OpenAlex
- W2090170171
- Cited by
- 238
- References
- 2
- FWCI
- 7.63
- Type
- conference paper
- Language
- en
- Open access
- green
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- Şimşek, Ö., Wolfe, A. P., & Barto, A. G. (2005). Identifying useful subgoals in reinforcement learning by local graph partitioning. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102454
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