The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
Open accessAbstract
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(λ) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(λ) and Q-learning belong.
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Citations
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Cited by
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Metadata
- Authors
- Jaakkola, T., Jordan, M. I., & Singh, S. P.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1994-11
- Volume
- 6
- Issue
- 6
- Pages
- 1185-1201
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2165131254
- Cited by
- 770
- References
- 4
- FWCI
- 20.37
- Type
- journal article
- Language
- en
- Open access
- green
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- Jaakkola, T., Jordan, M. I., & Singh, S. P. (1994). On the Convergence of Stochastic Iterative Dynamic Programming Algorithms. Neural Computation. https://doi.org/10.1162/neco.1994.6.6.1185
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