POLICY OPTIMIZATION PROVABLY CONVERGES TO NASH EQUILIBRIA IN ZERO-SUM LINEAR QUADRATIC GAMES

von Zhang, Kaiqing
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 15 of 20 (2020)

ON THE STABILITY AND CONVERGENCE OF ROBUST ADVERSARIAL REINFORCEMENT LEARNING: A CASE STUDY ON LINEAR QUADRATIC SYSTEMS

von Zhang, Kaiqing
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 27 of 27 (2021)

On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning

von Mao, Weichao
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 18 of 33 (2023)

POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis

von Mao, Weichao
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 6 of 27 (2021)

AN IMPROVED ANALYSIS OF (VARIANCE-REDUCED) POLICY GRADIENT AND NATURAL POLICY GRADIENT METHODS

von Liu, Yanli
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 10 of 27 (2021)

Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

von Zhang, Kaiqing
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 4 of 36 (2022)

What is a Good Metric to Study Generalization of Minimax Learners?

von Ozdaglar, Asuman
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 49 of 50 (2023)

Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes

von Ding, Dongsheng
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 11 of 27 (2021)

Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence

von Ding, Dongsheng
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 7 of 33 (2023)

Do Differentiable Simulators Give Better Policy Gradients?

von Suh, Hyung Ju
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 35 of 33 (2023)

Decentralized Q-learning in Zero-sum Markov Games

von Sayin, Muhammed
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 22 of 36 (2022)

Globally Convergent Policy Search for Output Estimation

von Umenberger, Jack
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 30 of 50 (2023)

NEAR-OPTIMAL MODEL-FREE REINFORCEMENT LEARNING IN NON-STATIONARY EPISODIC MDPS

von MAO, WEICHAO
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 10 of 16 (2022)

ROBUST MULTI-AGENT REINFORCEMENT LEARNING WITH MODEL UNCERTAINTY

von Zhang, Kaiqing
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 13 of 27 (2021)

REINFORCEMENT LEARNING FOR COST-AWARE MARKOV DECISION PROCESSES

von SUTTLE, WESLEY A.
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 13 of 16 (2022)