IEEE

Model-Free Q-Learning for Output Feedback Nash Strategy of Decentralized Nonzero-Sum Games

Abstract: In this article, we present a model-free output feedback (OPFB) Q-learning algorithm to find the optimal Nash equilibrium strategy for the decentralized control problem (DCP) of nonzero-sum ...
Frontiers

Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler

Genome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics. Although de novo assemblers have ...
IEEE

Optimizing Successive Over-relaxation Q-learning with Deterministic Perturbation Gradient Search

Abstract: Successive Over-Relaxation Q-learning (SOR-QL) has been proposed recently as an alternative to the widely popular Q-learning algorithm as it is seen to provide better performance where ...
Frontiers

Reinforcement learning based estimation of shortest paths in dynamically changing transportation networks

Finding the shortest path in a network is a classical problem, and a variety of search strategies have been proposed to solve it. In this paper, we review traditional approaches for finding shortest ...