Version Compatibility: R2021a and aboveToolbox Requirements: Reinforcement Learning Toolbox + Deep Learning Toolbox1. IntroductionDeep Reinforcement Learning (DRL) is a type of intelligent algorithm that combines deep learning with reinforcement learning. It has been widely applied in various fields such as robotics, game playing, and autonomous driving.
Version Compatibility: R2021a and above Toolbox Requirements: Reinforcement Learning Toolbox + Deep Learning Toolbox 1. Introduction Deep Reinforcement Learning (DRL) is a class of intelligent decision-making algorithms that combines deep learning with reinforcement learning. The core idea is to learn a policy that maximizes long-term cumulative rewards through interaction with the environment. Starting from R2019b, … Read more
Original Information Paper Title:Model Similarity Based Clustering Federated Learning in Edge Computing Accepted Conference:EAI CollaborateCom 2024 (CCF C) Author List 1) Liu Xiaoyan, China University (Beijing), School of Artificial Intelligence, PhD student of 2023 2) Huang Jiwei, China University (Beijing), School of Artificial Intelligence, Professor 3) Chen Ying, Beijing Information Science and Technology University, School … Read more
Recently, the research team at Shanghai AI Lab’s Embodied Intelligence Center achieved a breakthrough in robot control by proposing the HoST (Humanoid Standing-up Control) algorithm, which successfully enables humanoid robots to autonomously stand up in various complex environments while demonstrating strong anti-interference capabilities.This innovation not only addresses the challenge of transitioning robots from a sitting … Read more
The “Intelligence Paradox” of Embedded Systems Under the dual pressure of the slowing Moore’s Law and the exponential growth of the Internet of Things, embedded systems are facing historic challenges: How to achieve intelligence on hardware with limited resources (<1MB memory, mW-level power consumption)? Traditional views hold that AI will disrupt the embedded field. However, … Read more
Click on the blue textδΈ¨Follow us Source: Journal of Electronics and Information Authors: Zhou Xiaotian, Sun Shang, Zhang Haixia, Deng Yiqin, Lu Binbin Abstract Keywords AI quality inspection is an important part of smart manufacturing. The equipment generates a large number of computation-intensive and delay-sensitive tasks during product quality inspection. Due to insufficient computing power … Read more
In the wave of digital transformation in the construction industry, cognitive technology plays a core role. Traditional computer vision technologies can identify various equipment and physical environments on construction sites, but their limitation lies in the lack of proactive perception and decision-making capabilities. To address this, Xianyuan Technology has proposed the Agentic Multi-modal Cognition (AMC) … Read more
Click the blue text above to follow us πππ The content of this article is as follows: πππ Table of Contents π₯1 Overview π2 Results π3 References π4 Download Matlab Code, Data, and Article 1 Overview Source of literature: Abstract: Mobile Edge Computing (MEC) utilizes the computational power at the network edge to perform computation-intensive … Read more
Source: “China Electric Power”, 2024, Issue 1 Citation: Zhang Chao, Zhao Dongmei, Ji Yu, et al. Real-time Optimization Scheduling of Virtual Power Plants Based on Improved Deep Q-Networks [J]. China Electric Power, 2024, 57(1): 91-100. Click the “Read the original text” button at the bottom left corner of the article to view the full paper … Read more
Programmers, are you fighting bugs every day? Good news is here! Recently, the Doubao Team from ByteDance has made a significant move by open-sourcing a tool called Multi-SWE-bench. This is not just an ordinary tool; it is specifically designed to test the “automatic bug-fixing” capabilities of large models, and it supports multiple programming languages! Now … Read more