Abstract: Q-learning and double Q-learning are well-known sample-based, off-policy reinforcement learning algorithms. However, Q-learning suffers from overestimation bias, while double Q-learning ...

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 ...

A new technical paper titled “Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform” was published by researchers at TU Dresden, ScaDS.AI and Centre for Tactile ...

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...

Abstract: This paper presents a novel Q-learning algorithm to address the optimal load frequency control (LFC) problem in a single-area power system with unknown parameters. LFC is a critical issue ...

This guide encapsulates my journey in creating intelligent agents, focusing on a reinforcement learning approach, particularly using the Q-Star method. It offers a practical walkthrough of Microsoft's ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...