5 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…
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…
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…
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…