Cover Image

Gaussian-Mixture-Model Q-Functions for Policy Iteration in Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Vu, Minh, Slavakis, Konstantinos
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization
by: Vu, Minh, et al.
Published: (2024)

Online reinforcement learning via sparse Gaussian mixture model Q-functions
by: Vu, Minh, et al.
Published: (2025)

Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
by: Akiyama, Yuki, et al.
Published: (2024)

Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
by: Akiyama, Yuki, et al.
Published: (2025)

Nonconvex Regularization for Feature Selection in Reinforcement Learning
by: Suzuki, Kyohei, et al.
Published: (2025)

Robust Invariant Representation Learning by Distribution Extrapolation
by: Yoshida, Kotaro, et al.
Published: (2025)

Multilinear Kernel Regression and Imputation via Manifold Learning
by: Nguyen, Duc Thien, et al.
Published: (2024)

External Division of Two Bregman Proximity Operators for Poisson Inverse Problems
by: Haishima, Kazuki, et al.
Published: (2026)

Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning
by: Nguyen, Duc Thien, et al.
Published: (2024)

Kernel Regression of Multi-Way Data via Tensor Trains with Hadamard Overparametrization: The Dynamic Graph Flow Case
by: Nguyen, Duc Thien, et al.
Published: (2025)

Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
by: Lin, Guang, et al.
Published: (2025)

Feasible Policy Iteration for Safe Reinforcement Learning
by: Yang, Yujie, et al.
Published: (2023)

Iterative Refinement of Flow Policies in Probability Space for Online Reinforcement Learning
by: Sun, Mingyang, et al.
Published: (2025)

Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
by: Montenegro, Alessandro, et al.
Published: (2024)

Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search
by: Najib, Amna, et al.
Published: (2024)

Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2024)

Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization
by: Ding, Shutong, et al.
Published: (2024)

Structure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning
by: Champion, Théophile, et al.
Published: (2024)

Federated Gaussian Mixture Models
by: Pettersson, Sophia Zhang, et al.
Published: (2025)

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025)

One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Learning
by: Nguyen, Thanh, et al.
Published: (2025)

Understanding Self-Supervised Learning via Gaussian Mixture Models
by: Bansal, Parikshit, et al.
Published: (2024)

Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
by: Zhang, Ruoqi, et al.
Published: (2024)

Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
by: Doo, JaeHyeok, et al.
Published: (2026)

On the Role of Iterative Computation in Reinforcement Learning
by: Ghugare, Raj, et al.
Published: (2026)

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning
by: von Hartz, Jan Ole, et al.
Published: (2025)

SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
by: Samadi, Amir, et al.
Published: (2024)

Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning
by: Fang, Linjiajie, et al.
Published: (2024)

Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning
by: Manda, Kausthubh, et al.
Published: (2025)

Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning
by: Wu, Shuyuan, et al.
Published: (2024)

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
by: Alles, Marvin, et al.
Published: (2025)

QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
by: Zhang, Grace, et al.
Published: (2023)

Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function
by: Pham, Tuan Minh, et al.
Published: (2026)

Frozen Policy Iteration: Computationally Efficient RL under Linear $Q^π$ Realizability for Deterministic Dynamics
by: Ke, Yijing, et al.
Published: (2026)

Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
by: Emmanouilidis, Konstantinos, et al.
Published: (2026)

Graph-Regularized Learning of Gaussian Mixture Models
by: Abdurakhmanova, Shamsiiat, et al.
Published: (2025)

Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
by: Zhu, Menghao Waiyan William, et al.
Published: (2025)

Metacognitive Sensitivity for Test-Time Dynamic Model Selection
by: Trinh, Le Tuan Minh, et al.
Published: (2025)

Individual-heterogeneous sub-Gaussian Mixture Models
by: Qing, Huan
Published: (2026)

Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
by: Tayal, Mumuksh, et al.
Published: (2026)