If you have trouble following the instruction below, feel free to join OSCER weekly zoom help sessions. If you're doing deep learning neural network research, tensorflow need no introduction. It is ...

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...

This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...

Abstract: This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternative to the ...

Can an AI learn to play the perfect game of Snake? This video explores the capabilities of artificial intelligence in mastering the classic game, including the strategies and algorithms used in the ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

This project implements a Deep Q-Network (DQN) agent to play the Atari game Breakout using Stable Baselines3 and Gymnasium. Atari_Deep_Q_learning/ ├── train.py # Training script ├── play.py # ...

To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating Markov ...