The “Harvest Now, Decrypt Later” hacking strategy relies on the belief that the world is only a few years away from ...

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 ...

Every game of chess is a dialogue - A test of intention, creativity, and learning that echoes far beyond the board. “Chess Game” isn’t just another web-based chess app; it’s a bold experiment in ...

Caption:MIT researchers created a periodic table of machine learning that shows how more than 20 classical algorithms are connected. The new framework sheds light on how scientists could fuse ...

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 ...

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...

2024 IEEE International Conference on Quantum Computing and Engineering (QCE) In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential ...

Our article offers an answer to a foundational question in psychology and neuroscience: how do people learn from rewards and punishments? Specifically, we introduce a computational model of human ...

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 ...