Saved in:
| Main Authors: | Liu, Jiacai, Li, Wenye, Wei, Ke |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.03372 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Convergence of Policy Mirror Descent with Temporal Difference Evaluation
by: Liu, Jiacai, et al.
Published: (2025)
by: Liu, Jiacai, et al.
Published: (2025)
Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
by: Li, Wenye, et al.
Published: (2025)
by: Li, Wenye, et al.
Published: (2025)
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes
by: Liu, Jiacai, et al.
Published: (2023)
by: Liu, Jiacai, et al.
Published: (2023)
Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch
by: Wang, Weizhen, et al.
Published: (2025)
by: Wang, Weizhen, et al.
Published: (2025)
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
by: Feng, Jie, et al.
Published: (2024)
by: Feng, Jie, et al.
Published: (2024)
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
by: Klein, Sara, et al.
Published: (2023)
by: Klein, Sara, et al.
Published: (2023)
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
by: Ding, Dongsheng, et al.
Published: (2023)
by: Ding, Dongsheng, et al.
Published: (2023)
Policy Gradient Methods for Discrete Time Linear Quadratic Regulator With Random Parameters
by: Li, Deyue
Published: (2023)
by: Li, Deyue
Published: (2023)
A Concise Lyapunov Analysis of Nesterov's Accelerated Gradient Method
by: Liu, Jun
Published: (2025)
by: Liu, Jun
Published: (2025)
Model-Free Output Feedback Stabilization via Policy Gradient Methods
by: Zhang, Ankang, et al.
Published: (2026)
by: Zhang, Ankang, et al.
Published: (2026)
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)
by: Mukherjee, Amartya, et al.
Published: (2025)
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
by: Carmona, René, et al.
Published: (2019)
by: Carmona, René, et al.
Published: (2019)
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
by: Zeng, Sihan, et al.
Published: (2024)
by: Zeng, Sihan, et al.
Published: (2024)
Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis
by: Liu, Zijian
Published: (2025)
by: Liu, Zijian
Published: (2025)
A Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization
by: Xie, Furan, et al.
Published: (2025)
by: Xie, Furan, et al.
Published: (2025)
On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)
by: Liu, Zijian, et al.
Published: (2024)
Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control
by: Tabas, Sadegh Sadeghi, et al.
Published: (2024)
by: Tabas, Sadegh Sadeghi, et al.
Published: (2024)
On the Global Convergence of Risk-Averse Natural Policy Gradient Methods with Expected Conditional Risk Measures
by: Yu, Xian, et al.
Published: (2023)
by: Yu, Xian, et al.
Published: (2023)
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
by: Liu, Wei, et al.
Published: (2025)
by: Liu, Wei, et al.
Published: (2025)
Recurrent Natural Policy Gradient for POMDPs
by: Cayci, Semih, et al.
Published: (2024)
by: Cayci, Semih, et al.
Published: (2024)
Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)
by: Liu, Zijian, et al.
Published: (2023)
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
by: Ganesh, Swetha, et al.
Published: (2024)
by: Ganesh, Swetha, et al.
Published: (2024)
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
by: Liu, Rui, et al.
Published: (2024)
by: Liu, Rui, et al.
Published: (2024)
Gradient Methods with Online Scaling
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
by: Liu, Zijian
Published: (2026)
by: Liu, Zijian
Published: (2026)
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
by: Chen, Jun, et al.
Published: (2023)
by: Chen, Jun, et al.
Published: (2023)
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
by: Han, Yinbin, et al.
Published: (2023)
by: Han, Yinbin, et al.
Published: (2023)
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
by: Liu, Xin, et al.
Published: (2022)
by: Liu, Xin, et al.
Published: (2022)
Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework
by: Nanda, Phalguni, et al.
Published: (2026)
by: Nanda, Phalguni, et al.
Published: (2026)
Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods
by: Tao, Yuanzhe, et al.
Published: (2024)
by: Tao, Yuanzhe, et al.
Published: (2024)
Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)
by: Chen, Lesi, et al.
Published: (2025)
Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
by: Zhang, Runyu, et al.
Published: (2023)
by: Zhang, Runyu, et al.
Published: (2023)
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)
by: Liu, Zijian, et al.
Published: (2025)
Structure Matters: Dynamic Policy Gradient
by: Klein, Sara, et al.
Published: (2024)
by: Klein, Sara, et al.
Published: (2024)
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
by: Xiao, Minheng, et al.
Published: (2024)
by: Xiao, Minheng, et al.
Published: (2024)
Delightful Policy Gradient
by: Osband, Ian
Published: (2026)
by: Osband, Ian
Published: (2026)
Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
by: Chiang, John
Published: (2022)
by: Chiang, John
Published: (2022)
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
by: Ding, Kuangyu, et al.
Published: (2023)
by: Ding, Kuangyu, et al.
Published: (2023)
Similar Items
-
On the Convergence of Policy Mirror Descent with Temporal Difference Evaluation
by: Liu, Jiacai, et al.
Published: (2025) -
Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
by: Li, Wenye, et al.
Published: (2025) -
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes
by: Liu, Jiacai, et al.
Published: (2023) -
Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch
by: Wang, Weizhen, et al.
Published: (2025) -
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
by: Feng, Jie, et al.
Published: (2024)