Summary: A paradigm-shifting study has upended a decades-long neurological assumption that learning speed depends entirely on repetition and experience rather than the size of a reward. The research ...

Overview: Qiskit remains the world’s most widely used quantum SDK for research and enterprise projects.AI and quantum ...

The semiconductor industry is known for its complex production. Thousands of machines (tools) perform thousands of operations over a diverse range of products with re-entrant flows and shifting ...

This project is "pre-alpha", and is not yet stable or fully realized. Use with caution, as the API and functionality are subject to significant changes. qBraid Algorithms provides a collection of ...

Recent advancements in LLMs such as OpenAI-o1, DeepSeek-R1, and Kimi-1.5 have significantly improved their performance on complex mathematical reasoning tasks. Reinforcement Learning with Verifiable ...

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 ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We present an artificial intelligence-guided approach to design durable and chemically ...

This project aims at reproducing the results of the paper Mean Field Multi-Agent Reinforcement Learning by Yang et al. (2018). The paper proposes two novel algorithms, MF-Q and MF-AC for multi-agent ...

Abstract: Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have ...

Deep learning technology has been widely applied to multi-source data and imaging in the past decade. It aims to handle multi-modality data from different sources including images, text, audio and ...