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 ...

MATLAB Programming is a high-level language and interactive environment used by millions of engineers and scientists worldwide. It enables numerical computation, visualization, and programming in a ...

An artificial-intelligence algorithm that discovers its own way to learn achieves state-of-the-art performance, including on some tasks it had never encountered before. Joel Lehman is at Lila Sciences ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

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 ...

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 ...

This important study uses reinforcement learning to study how turbulent odor stimuli should be processed to yield successful navigation. The authors find that there is an optimal memory length over ...

ABSTRACT: Diabetic retinopathy (DR), a leading cause of vision impairment worldwide, primarily impacts individuals with diabetes, making early detection vital to prevent irreversible vision loss.

Abstract: In this paper, we propose two simulations designed for the implementation of Q-learning on path planning. The first simulation of a modeling design using MatLab to find the best route by ...