Introduction: The learning process is characterized by its variability rather than linearity, as individuals differ in how they receive, process, and store information. In traditional learning, taking ...

Abstract: This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, ...

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 ...

Cardano (ADA) is still in negative territory, unable to rebound as the broader crypto market struggles to recover from a dramatic crash this week that led to approximately $110 billion being erased ...

XRP is back above the $3 mark after a shaky few weeks, regaining momentum and breathing new hope into the market. Hovering at around $3.04 at the time of writing, the cryptocurrency is up over 1.72% ...

Ms. Anderson and Ms. Winthrop are the authors of “The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better.” As the new school year gets underway, artificial intelligence is ...

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 ...

We used a RL algorithm called Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE) to maximize cumulative returns and evaluated the model using a doubly robust off-policy ...

Patent applications on artificial intelligence and machine learning have soared in recent years, yet legal guidance on the patentability of AI and machine learning algorithms remains scarce. The US ...

Framework Design: Designed and implemented an efficient multi-agent DRL training framework for cooperative observation scenarios. 🧠 Probabilistic Estimation: Proposed a probabilistic matrix ...