12 papers · 1 filter
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…
Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions
Kyohei Suzuki, Konstantinos Slavakis
This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions. On the RL side, the…
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…
External Division of Two Bregman Proximity Operators for Poisson Inverse Problems
Kazuki Haishima, Kyohei Suzuki, Konstantinos Slavakis
This paper presents a novel method for recovering sparse vectors from linear models corrupted by Poisson noise. The contribution is twofold. First, an operator defined via the exte…
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…