1 citations · 1 across the 7 of their papers we have counts for
7 papers
Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning
Minh Vu, Konstantinos Slavakis
This paper develops an online, off-policy policy-iteration framework for reinforcement learning (RL), based on sparse Gaussian-mixture-model Q-functions (S-GMM-QFs). The framework…
Gaussian-Mixture-Model Q-Functions for Policy Iteration in Reinforcement Learning
Minh Vu, Konstantinos Slavakis
Unlike their conventional use as estimators of probability density functions in reinforcement learning (RL), this paper introduces a novel function-approximation role for Gaussian…
A Multi-Criterion Approach to Smart EV Charging with CO2 Emissions and Cost Minimization
Giuseppe C. Calafiore, Luca Ambrosino, Khai Manh Nguyen +3
We study carbon-aware smart charging in a fossil-dominated grid by coupling a simplified hydro-thermal-renewable dispatch model with a tractable linear charging scheduler. The case…
Online reinforcement learning via sparse Gaussian mixture model Q-functions
Minh Vu, Konstantinos Slavakis
This paper introduces a structured and interpretable online policy-iteration framework for reinforcement learning (RL), built around the novel class of sparse Gaussian mixture mode…
Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
Yuki Akiyama, Konstantinos Slavakis
This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected…
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization
Minh Vu, Konstantinos Slavakis
This paper establishes a novel role for Gaussian-mixture models (GMMs) as functional approximators of Q-function losses in reinforcement learning (RL). Unlike the existing RL liter…
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich ap…