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OnActor-Critic Algorithms
Abstract
In this article, we propose and analyze a class of actor-critic algorithms. These are two-time-scale algorithms in which the critic uses temporal difference learning with a linearly parameterized approximation architecture, and the actor is updated in an approximate gradient direction, based on information provided by the critic. We show that the features for the critic should ideally span a subspace prescribed by the choice of parameterization of the actor. We study actor-critic algorithms for Markov decision processes with Polish state and action spaces. We state and prove two results regarding their convergence.
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Cited by
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Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733
Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv · doi:10.48550/arxiv.1802.09477 · Cited by 3,650
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1506.02438 · Cited by 2,103
Intelligent 5G: When Cellular Networks Meet Artificial Intelligence
Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600304wc · Cited by 601
Off-Policy Deep Reinforcement Learning without Exploration
Fujimoto, S., Meger, D., & Precup, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02900 · Cited by 268
Metadata
- Authors
- Konda, V. R., & Tsitsiklis, J. N.
- Venue
- SIAM Journal on Control and Optimization
- Published
- 2003-01
- Volume
- 42
- Issue
- 4
- Pages
- 1143-1166
- ISSN
- 0363-0129 (ISSN-L)
- 1095-7138
- OpenAlex
- W2009303086
- Cited by
- 709
- References
- 0
- FWCI
- 19.67
- Type
- journal article
- Language
- en
- Open access
- closed
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