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Actor-Critic--Type Learning Algorithms for Markov Decision Processes
Abstract
Algorithms for learning the optimal policy of a Markov decision process (MDP) based on simulated transitions are formulated and analyzed. These are variants of the well-known "actor-critic" (or "adaptive critic") algorithm in the artificial intelligence literature. Distributed asynchronous implementations are considered. The analysis involves two time scale stochastic approximations.
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Cited by
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042
Konda, V. R., & Tsitsiklis, J. N. (2003) · SIAM Journal on Control and Optimization · doi:10.1137/s0363012901385691 · Cited by 709
Self-citationNatural actor–critic algorithms
Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M., & Lee, M. (2009) · Automatica · doi:10.1016/j.automatica.2009.07.008 · Cited by 559
Reinforcement Learning: A Tutorial Survey and Recent Advances
Gosavi, A. (2009) · INFORMS Journal on Computing · doi:10.1287/ijoc.1080.0305 · Cited by 326
LOGAN: Latent Optimisation for Generative Adversarial Networks
Wu, Y., Donahue, J., Balduzzi, D., Simonyan, K., & Lillicrap, T. (2019) · arXiv · doi:10.48550/arxiv.1912.00953
Metadata
- Authors
- Konda, V. R., & Borkar, V. S.
- Venue
- SIAM Journal on Control and Optimization
- Published
- 1999-01
- Volume
- 38
- Issue
- 1
- Pages
- 94-123
- ISSN
- 0363-0129 (ISSN-L)
- 1095-7138
- OpenAlex
- W2082261506
- Cited by
- 234
- References
- 0
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- Konda, V. R., & Borkar, V. S. (1999). Actor-Critic--Type Learning Algorithms for Markov Decision Processes. SIAM Journal on Control and Optimization. https://doi.org/10.1137/s036301299731669x
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