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 ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms as well as supporting myriad research applications. Of particular interest ...

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.

1 Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia 2 Research Institute for Applied Artificial Intelligence and Digital ...

In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy ...

The same is true for Q# callables defined in Jupyter notebook using the %%qsharp cell magic: These callables can then be invoked as normal Python functions, which will run them in the Q# simulator ...

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 ...

Institute of Logistics Science and Engineering of Shanghai Maritime University, Pudong, China Introduction: This study addresses the joint scheduling optimization of continuous berths and quay cranes ...

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 ...