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Publications about 'Policy gradient methods'
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D. Ding.
Provable reinforcement learning for constrained and multi-agent control systems.
PhD thesis,
University of Southern California,
2022.
Keyword(s): Constrained Markov decision processes,
Constrained nonconvex optimization,
Function approximation,
Game-agnostic convergence,
Multi-agent reinforcement learning,
Multi-agent systems,
Natural policy gradient,
Policy gradient methods,
Proximal policy optimization,
Primal-dual algorithms,
Reinforcement learning,
Safe exploration,
Safe reinforcement learning,
Sample complexity,
Stochastic optimization.
[bibtex-entry]
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D. Ding,
K. Zhang,
J. Duan,
T. Basar,
and M. R. Jovanovic.
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs.
J. Mach. Learn. Res.,
2022.
Note: Submitted; also arXiv:2206.02346.
Keyword(s): Constrained Markov decision processes,
Constrained nonconvex optimization,
Function approximation,
Natural policy gradient,
Policy gradient methods,
Primal-dual algorithms,
Sample complexity.
[bibtex-entry]
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D. Ding and M. R. Jovanovic.
Policy gradient primal-dual mirror descent for constrained MDPs with large state spaces.
In Proceedings of the 61st IEEE Conference on Decision and Control,
Cancun, Mexico,
pages 4892-4897,
2022.
Keyword(s): Constrained Markov decision processes,
Policy gradient methods,
Primal-dual algorithms,
Mirror descent,
Function approximation.
[bibtex-entry]
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D. Ding,
C.-Y. Wei,
K. Zhang,
and M. R. Jovanovic.
Independent policy gradient for large-scale Markov potential games: sharper rates, function approximation, and game-agnostic convergence.
In Proceedings of the 39th International Conference on Machine Learning,
volume 162 of Proceedings of Machine Learning Research,
Baltimore, MD,
pages 5166-5220,
2022.
Keyword(s): Multi-agent reinforcement learning,
Independent reinforcement learning,
Policy gradient methods,
Markov potential games,
Function approximation,
Game-agnostic convergence.
[bibtex-entry]
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D. Ding,
K. Zhang,
T. Basar,
and M. R. Jovanovic.
Convergence and optimality of policy gradient primal-dual method for constrained Markov decision processes.
In Proceedings of the 2022 American Control Conference,
Atlanta, GA,
pages 2851-2856,
2022.
Keyword(s): Constrained Markov decision processes,
Policy gradient methods,
Primal-dual algorithms.
[bibtex-entry]
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D. Ding,
K. Zhang,
T. Basar,
and M. R. Jovanovic.
Natural policy gradient primal-dual method for constrained Markov decision processes.
In Proceedings of the 34th Conference on Neural Information Processing Systems,
volume 33,
Vancouver, Canada,
pages 8378-8390,
2020.
Keyword(s): Constrained Markov decision processes,
Constrained nonconvex optimization,
Natural policy gradient,
Policy gradient methods,
Primal-dual algorithms.
[bibtex-entry]
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