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Publications about 'Proximal policy optimization'
Theses
  1. 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]


Conference articles
  1. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Provably efficient safe exploration via primal-dual policy optimization. In 24th International Conference on Artificial Intelligence and Statistics, volume 130, Virtual, pages 3304-3312, 2021. Keyword(s): Safe reinforcement learning, Constrained Markov decision processes, Safe exploration, Proximal policy optimization, Non-convex optimization, Online mirror descent, Primal-dual method. [bibtex-entry]



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Last modified: Sun Oct 23 23:45:07 2022
Author: mihailo.


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