Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
Fuente:
arXiv
Saved in:
| Main Authors: | Park, Jaehyun, Kwon, Junyeop, Lee, Dabeen |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation
by: Chae, Woojin, et al.
Published: (2024)
by: Chae, Woojin, et al.
Published: (2024)
Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
by: Park, Sungwoo, et al.
Published: (2024)
by: Park, Sungwoo, et al.
Published: (2024)
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
by: Chae, Woojin, et al.
Published: (2024)
by: Chae, Woojin, et al.
Published: (2024)
Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism
by: Yu, Kihyun, et al.
Published: (2024)
by: Yu, Kihyun, et al.
Published: (2024)
Stochastic-Constrained Stochastic Optimization with Markovian Data
by: Kim, Yeongjong, et al.
Published: (2023)
by: Kim, Yeongjong, et al.
Published: (2023)
Learning Weakly Communicating Average-Reward CMDPs: Strong Duality and Improved Regret
by: Yu, Kihyun, et al.
Published: (2026)
by: Yu, Kihyun, et al.
Published: (2026)
Near-Optimal Primal-Dual Algorithm for Learning Linear Mixture CMDPs with Adversarial Rewards
by: Yu, Kihyun, et al.
Published: (2026)
by: Yu, Kihyun, et al.
Published: (2026)
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
by: Angiuli, Andrea, et al.
Published: (2023)
by: Angiuli, Andrea, et al.
Published: (2023)
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
by: Li, Jingqi, et al.
Published: (2022)
by: Li, Jingqi, et al.
Published: (2022)
Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
by: Yoon, Hoyeol, et al.
Published: (2026)
by: Yoon, Hoyeol, et al.
Published: (2026)
Reinforcement Learning with Function Approximation for Non-Markov Processes
by: Kara, Ali Devran
Published: (2026)
by: Kara, Ali Devran
Published: (2026)
Reinforcement Learning with Random Time Horizons
by: Borrell, Enric Ribera, et al.
Published: (2025)
by: Borrell, Enric Ribera, et al.
Published: (2025)
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
by: Cai, Qi, et al.
Published: (2022)
by: Cai, Qi, et al.
Published: (2022)
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
by: Zhao, Heyang, et al.
Published: (2023)
by: Zhao, Heyang, et al.
Published: (2023)
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
by: Kwon, Jeongyeol, et al.
Published: (2023)
by: Kwon, Jeongyeol, et al.
Published: (2023)
A Finite-Time Analysis of TD Learning with Linear Function Approximation without Projections or Strong Convexity
by: Lee, Wei-Cheng, et al.
Published: (2025)
by: Lee, Wei-Cheng, et al.
Published: (2025)
Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learning
by: Shen, Kaichen, et al.
Published: (2026)
by: Shen, Kaichen, et al.
Published: (2026)
Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs
by: Zamir, Guy, et al.
Published: (2026)
by: Zamir, Guy, et al.
Published: (2026)
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
by: Ke, Zhifa, et al.
Published: (2023)
by: Ke, Zhifa, et al.
Published: (2023)
Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints
by: Sarkar, Dhruv, et al.
Published: (2025)
by: Sarkar, Dhruv, et al.
Published: (2025)
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by: Cho, Wooseong, et al.
Published: (2024)
by: Cho, Wooseong, et al.
Published: (2024)
Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)
by: Qian, Xiaochi, et al.
Published: (2024)
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
by: Wang, Han, et al.
Published: (2023)
by: Wang, Han, et al.
Published: (2023)
A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
by: Mitra, Aritra
Published: (2024)
by: Mitra, Aritra
Published: (2024)
Two-Timescale Linear Stochastic Approximation: Constant Stepsizes Go a Long Way
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026)
by: Liu, Xinyu, et al.
Published: (2026)
The Method of Infinite Descent
by: Batley, Reza T., et al.
Published: (2025)
by: Batley, Reza T., et al.
Published: (2025)
Mixed-feature Logistic Regression Robust to Distribution Shifts
by: Sun, Qingshi, et al.
Published: (2025)
by: Sun, Qingshi, et al.
Published: (2025)
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation
by: Lee, Joo Seung, et al.
Published: (2024)
by: Lee, Joo Seung, et al.
Published: (2024)
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
by: Sherman, Uri, et al.
Published: (2025)
by: Sherman, Uri, et al.
Published: (2025)
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
by: Lai, Kuo-Wei, et al.
Published: (2023)
by: Lai, Kuo-Wei, et al.
Published: (2023)
Predictor-Based Output-Feedback Control of Linear Systems with Time-Varying Input and Measurement Delays via Neural-Approximated Prediction Horizons
by: Bhan, Luke, et al.
Published: (2026)
by: Bhan, Luke, et al.
Published: (2026)
Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
by: Kale, Sacchit, et al.
Published: (2026)
by: Kale, Sacchit, et al.
Published: (2026)
Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
by: Hong, Kihyuk, et al.
Published: (2024)
by: Hong, Kihyuk, et al.
Published: (2024)
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)
by: Selvi, Aras, et al.
Published: (2024)
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
by: Gopalani, Pulkit, et al.
Published: (2023)
by: Gopalani, Pulkit, et al.
Published: (2023)
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
by: Cayci, Semih, et al.
Published: (2021)
by: Cayci, Semih, et al.
Published: (2021)
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
by: Hwang, Taehyun, et al.
Published: (2022)
by: Hwang, Taehyun, et al.
Published: (2022)
Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
by: Meng, Si Yi, et al.
Published: (2024)
by: Meng, Si Yi, et al.
Published: (2024)
Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
by: Long, Kehan, et al.
Published: (2025)
by: Long, Kehan, et al.
Published: (2025)
Similar Items
-
Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation
by: Chae, Woojin, et al.
Published: (2024) -
Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
by: Park, Sungwoo, et al.
Published: (2024) -
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
by: Chae, Woojin, et al.
Published: (2024) -
Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism
by: Yu, Kihyun, et al.
Published: (2024) -
Stochastic-Constrained Stochastic Optimization with Markovian Data
by: Kim, Yeongjong, et al.
Published: (2023)