Cover Image

Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Amiri, Mohsen, Magnússon, Sindri
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Human-Inspired Framework to Accelerate Reinforcement Learning
by: Beikmohammadi, Ali, et al.
Published: (2023)

PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC
by: Amiri, Mohsen, et al.
Published: (2026)

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
by: Luo, Baiting, et al.
Published: (2024)

Challenger-Based Combinatorial Bandits for Subcarrier Selection in OFDM Systems
by: Amiri, Mohsen, et al.
Published: (2025)

Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes
by: Morimura, Tetsuro, et al.
Published: (2022)

On the Convergence of Federated Learning Algorithms without Data Similarity
by: Beikmohammadi, Ali, et al.
Published: (2024)

Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints
by: Vaishnav, Shubham, et al.
Published: (2025)

Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning
by: Vaishnav, Shubham, et al.
Published: (2025)

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
by: Sanokowski, Sebastian, et al.
Published: (2025)

SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
by: Kharazian, Zahra, et al.
Published: (2024)

MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment
by: Amiri, Mohsen, et al.
Published: (2025)

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes
by: Vora, Kevin, et al.
Published: (2025)

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
by: Infante, Guillermo, et al.
Published: (2021)

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
by: Bai, Qinbo, et al.
Published: (2023)

Inverse Reinforcement Learning from Non-Stationary Learning Agents
by: Sivakumar, Kavinayan P., et al.
Published: (2024)

A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data
by: Beikmohammadi, Ali, et al.
Published: (2024)

Online Reinforcement Learning in Non-Stationary Context-Driven Environments
by: Hamadanian, Pouya, et al.
Published: (2023)

Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes
by: Kumar, Navdeep, et al.
Published: (2025)

Optimal Decision Tree Policies for Markov Decision Processes
by: Vos, Daniël, et al.
Published: (2023)

Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?
by: Gerogiannis, Argyrios, et al.
Published: (2024)

Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes
by: Rojas, Juan Sebastian, et al.
Published: (2024)

Decision Making in Non-Stationary Environments with Policy-Augmented Search
by: Pettet, Ava, et al.
Published: (2024)

Markov Decision Processes under External Temporal Processes
by: Ayyagari, Ranga Shaarad, et al.
Published: (2023)

Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
by: Wang, Tongxi, et al.
Published: (2026)

On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
by: Murthy, Yashaswini, et al.
Published: (2023)

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes
by: Trapasso, Alessandro, et al.
Published: (2025)

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by: Moon, Sang Bin, et al.
Published: (2024)

Policy Gradient for Robust Markov Decision Processes
by: Wang, Qiuhao, et al.
Published: (2024)

Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
by: Afshar, Aida, et al.
Published: (2024)

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
by: Mondal, Washim Uddin, et al.
Published: (2023)

TIMRL: A Novel Meta-Reinforcement Learning Framework for Non-Stationary and Multi-Task Environments
by: Qi, Chenyang, et al.
Published: (2025)

GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model
by: Zhou, Zhehua, et al.
Published: (2024)

A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
by: Banse, Adrien, et al.
Published: (2024)

In-Context Learning for Non-Stationary MIMO Equalization
by: Jiang, Jiachen, et al.
Published: (2025)

SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
by: Xiong, Xuyuan, et al.
Published: (2025)

Solving Robust Markov Decision Processes: Generic, Reliable, Efficient
by: Meggendorfer, Tobias, et al.
Published: (2024)

Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
by: Infante, Guillermo, et al.
Published: (2024)

Curriculum Learning for LLM Pretraining: An Analysis of Learning Dynamics
by: Elgaar, Mohamed, et al.
Published: (2026)

Compressed Federated Reinforcement Learning with a Generative Model
by: Beikmohammadi, Ali, et al.
Published: (2024)