The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Efficient Exploration in Reinforcement Learning
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Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Baxter, L. A. (1995) · Technometrics · doi:10.1080/00401706.1995.10484354 · Cited by 2,415
Neuro-Dynamic Programming: NDP
Bertsekas, D. P. (2001) · Encyclopedia of Optimization · doi:10.1007/0-306-48332-7_333 · Cited by 1,466
On the Sample Complexity of Reinforcement Learning
Kakade, S. M. (2003) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 502
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
Kearns, M., Mansour, Y., & Ng, A. Y. (2002) · Machine Learning · doi:10.1023/a:1017932429737 · Cited by 488
Predictive Representations of State
Littman, M. L., & Sutton, R. S. (2001) · Cited by 425
On the Complexity of Solving Markov Decision Problems
Littman, M. L., Dean, T., & Kaelbling, L. P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4971 · Cited by 424
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
Brafman, R. I., & Tennenholtz, M. (2001) · Cited by 366
PEGASUS: A Policy Search Method for Large MDPs and POMDPs
Ng, A. Y., & Jordan, M. I. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3878 · Cited by 360
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
Mannor, S., & Tsitsiklis, J. N. (2004) · doi:10.5555/1005332.1005355 · Cited by 307
PAC Bounds for Multi-armed Bandit and Markov Decision Processes
Even-Dar, E., Mannor, S., & Mansour, Y. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45435-7_18 · Cited by 298
Model-Based Bayesian Exploration
Dearden, R., Friedman, N., & André, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6690 · Cited by 221
Efficient reinforcement learning in factored MDPs
Kearns, M. J., & Koller, D. (1999) · Cited by 142
Policy Search by Dynamic Programming
Bagnell, J. A., Kakade, S. M., Ng, A. Y., & Schneider, J. (2018) · ScholarlyCommons (University of Pennsylvania) · doi:10.1184/r1/6558239 · Cited by 125
Exploration in metric state spaces
Kakade, S. M., Kearns, M. J., & Langford, J. C. (2003) · ScholarlyCommons (University of Pennsylvania) · Cited by 107
Approximate Policy Iteration with a Policy Language Bias
Fern, A., Yoon, S., & Givan, R. L. (2003) · Cited by 87
Empirical comparison of various reinforcement learning strategies for sequential targeted marketing
Abe, N., Pednault, E., Wang, H., Zadrozny, B., Fan, W., & Apte, C. (2003) · Proceedings · doi:10.1109/icdm.2002.1183879 · Cited by 19
Cited by
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Fuzzy inference system learning by reinforcement methods
Jouffe, L. (1998) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/5326.704563 · Cited by 384
Off-Policy Reinforcement Learning for $ H_\infty $ Control Design
Luo, B., Wu, H.-N., & Huang, T. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2319577 · Cited by 362
Probabilistic 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
A Survey of Exploration Methods in Reinforcement Learning
Amin, S., Gomrokchi, M., Satija, H., van Hoof, H., & Precup, D. (2021) · arXiv · doi:10.48550/arxiv.2109.00157 · Cited by 41
Metadata
- Venue
- SpringerReference
- Publisher
- Springer-Verlag
- Published
- 2012-02-07
- OpenAlex
- W1504212531
- Cited by
- 257
- References
- 17
- Type
- reference entry
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
