Reinforcement Learning with Function Approximation for Non-Markov Processes
Fuente:
arXiv
Saved in:
| Main Author: | Kara, Ali Devran |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning POMDPs with Linear Function Approximation and Finite Memory
by: Kara, Ali Devran
Published: (2025)
by: Kara, Ali Devran
Published: (2025)
Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
by: Kara, Ali Devran, et al.
Published: (2024)
by: Kara, Ali Devran, et al.
Published: (2024)
Non-stationary and Varying-discounting Markov Decision Processes for Reinforcement Learning
by: Chen, Zhizuo, et al.
Published: (2025)
by: Chen, Zhizuo, et al.
Published: (2025)
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments
by: Kara, Ali Devran, et al.
Published: (2023)
by: Kara, Ali Devran, et al.
Published: (2023)
Near Optimal Approximations and Finite Memory Policies for POMPDs with Continuous Spaces
by: Kara, Ali Devran, et al.
Published: (2024)
by: Kara, Ali Devran, et al.
Published: (2024)
Finite Approximations for Mean Field Type Multi-Agent Control and Their Near Optimality
by: Bayraktar, Erhan, et al.
Published: (2022)
by: Bayraktar, Erhan, et al.
Published: (2022)
Quantizer Design for Finite Model Approximations, Model Learning, and Quantized Q-Learning for MDPs with Unbounded Spaces
by: Bicer, Osman, et al.
Published: (2025)
by: Bicer, Osman, et al.
Published: (2025)
Refined Bounds on Near Optimality Finite Window Policies in POMDPs and Their Reinforcement Learning
by: Demirci, Yunus Emre, et al.
Published: (2024)
by: Demirci, Yunus Emre, et al.
Published: (2024)
Weakly Time-Coupled Approximation of Markov Decision Processes
by: Soheili, Negar, et al.
Published: (2026)
by: Soheili, Negar, et al.
Published: (2026)
Online Reinforcement Learning in Markov Decision Process Using Linear Programming
by: Leon, Vincent, et al.
Published: (2023)
by: Leon, Vincent, et al.
Published: (2023)
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
by: Mazumdar, Abhijit, et al.
Published: (2024)
by: Mazumdar, Abhijit, et al.
Published: (2024)
Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
by: Park, Jaehyun, et al.
Published: (2024)
by: Park, Jaehyun, et al.
Published: (2024)
Risk-sensitive Markov Decision Process and Learning under General Utility Functions
by: Wu, Zhengqi, et al.
Published: (2023)
by: Wu, Zhengqi, et al.
Published: (2023)
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 Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
by: Wan, Yi, et al.
Published: (2024)
by: Wan, Yi, et al.
Published: (2024)
Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control
by: Demirci, Yunus Emre, et al.
Published: (2025)
by: Demirci, Yunus Emre, et al.
Published: (2025)
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
by: Ke, Zhifa, et al.
Published: (2023)
by: Ke, Zhifa, et al.
Published: (2023)
Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
by: Xie, Yilin, et al.
Published: (2024)
by: Xie, Yilin, et al.
Published: (2024)
Online Markov Decision Processes with Terminal Law Constraints
by: Moreno, Bianca Marin, et al.
Published: (2026)
by: Moreno, Bianca Marin, et al.
Published: (2026)
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)
Learning with Linear Function Approximations in Mean-Field Control
by: Bayraktar, Erhan, et al.
Published: (2024)
by: Bayraktar, Erhan, et al.
Published: (2024)
Approximate Linear Programming for Decentralized Policy Iteration in Cooperative Multi-agent Markov Decision Processes
by: Mandal, Lakshmi, et al.
Published: (2023)
by: Mandal, Lakshmi, et al.
Published: (2023)
Optimal Sample Complexity for Average Reward Markov Decision Processes
by: Wang, Shengbo, et al.
Published: (2023)
by: Wang, Shengbo, et al.
Published: (2023)
Flipping-based Policy for Chance-Constrained Markov Decision Processes
by: Shen, Xun, et al.
Published: (2024)
by: Shen, Xun, 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)
Average Cost Optimality of Partially Observed MDPS: Contraction of Non-linear Filters, Optimal Solutions and Approximations
by: Demirci, Yunus Emre, et al.
Published: (2023)
by: Demirci, Yunus Emre, et al.
Published: (2023)
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)
Efficient Algorithms for Robust Markov Decision Processes with $s$-Rectangular Ambiguity Sets
by: Ho, Chin Pang, et al.
Published: (2026)
by: Ho, Chin Pang, et al.
Published: (2026)
A Simple Finite-Time Analysis of TD Learning with Linear Function Approximation
by: Mitra, Aritra
Published: (2024)
by: Mitra, Aritra
Published: (2024)
Achieving Instance-dependent Sample Complexity for Constrained Markov Decision Process
by: Jiang, Jiashuo, et al.
Published: (2024)
by: Jiang, Jiashuo, 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)
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
by: Rathore, Pratik, et al.
Published: (2025)
by: Rathore, Pratik, et al.
Published: (2025)
Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
by: Wang, Shengbo, et al.
Published: (2025)
by: Wang, Shengbo, et al.
Published: (2025)
Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
by: Xu, Mingyuan, et al.
Published: (2026)
by: Xu, Mingyuan, et al.
Published: (2026)
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)
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)
Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
by: Mahmoudinazlou, Sasan, et al.
Published: (2024)
by: Mahmoudinazlou, Sasan, et al.
Published: (2024)
Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
by: Kitamura, Toshinori, et al.
Published: (2024)
by: Kitamura, Toshinori, 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)
Similar Items
-
Learning POMDPs with Linear Function Approximation and Finite Memory
by: Kara, Ali Devran
Published: (2025) -
Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
by: Kara, Ali Devran, et al.
Published: (2024) -
Non-stationary and Varying-discounting Markov Decision Processes for Reinforcement Learning
by: Chen, Zhizuo, et al.
Published: (2025) -
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments
by: Kara, Ali Devran, et al.
Published: (2023) -
Near Optimal Approximations and Finite Memory Policies for POMPDs with Continuous Spaces
by: Kara, Ali Devran, et al.
Published: (2024)