Results 101 to 110 of about 6,304,901 (198)

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

CoRR
Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system utilization.
Matthew Sgambati, Aleksandar Vakanski, Matthew Anderson   +2 more
openaire   +3 more sources

Model-Free In Situ Optimization of Diffractive Optical Processors Using Proximal Policy Optimization

We present a model-free in situ training framework for diffractive optical networks based on Proximal Policy Optimization (PPO). This approach provides a scalable and robust solution for training diverse analog optical and physical computing systems.
Yuhang Li, Aydogan Ozcan, Shiqi Chen, Tingyu Gong   +3 more
core   +1 more source

An improved hybrid policy optimization method for economic-preference dispatch considering cross time-scales collaboration

International Journal of Electrical Power & Energy Systems
In order to achieve real-time active power dispatch (RAPD) efficiently and economically in a large-scale power system with high proportion of renewable energy, deep reinforcement learning (DRL) based dispatching agent is constructed for dynamic decision ...
Qianli Zhang, Hao Tang, Duanchao Li
doaj   +1 more source

Generating adversarial deep reinforcement learning -based frequency control of Island City microgrid considering generalization of scenarios

Frontiers in Energy Research
The increasing incorporation of new energy sources into power grids introduces significant variability, complicating traditional load frequency control (LFC) methods. This variability can cause frequent load disturbances and severe frequency fluctuations
Houtianfu Wang, Zhecong Zhang, Qixin Wang   +2 more
doaj   +1 more source

Federated proximal policy optimization with action masking: Application in collective heating systems

Energy and AI
This paper introduces a novel privacy-aware Federated Proximal Policy Optimization (FPPO) method combined with action masking. As a Federated Reinforcement Learning (FRL) approach, the proposed method is used for optimizing the reloading of Domestic Hot ...
Sara Ghane, Stef Jacobs, Furkan Elmaz, Thomas Huybrechts, Ivan Verhaert, Siegfried Mercelis   +5 more
doaj   +1 more source

Improving Proximal Policy Optimization Algorithm in Interactive Multi-Agent Systems [PDF]

Proximal Policy Optimization (PPO), as an outstanding Reinforcement learning (RL) algorithm, has proven its efficiency when solving a wide range of problems.
Shang, Yi, Cruz, Francisco, Chen, Yifei; id_orcid   +2 more
core   +1 more source

Preference learning based deep reinforcement learning for flexible job shop scheduling problem

Complex & Intelligent Systems
The flexible job shop scheduling problem (FJSP) holds significant importance in both theoretical research and practical applications. Given the complexity and diversity of FJSP, improving the generalization and quality of scheduling methods has become a ...
Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao   +4 more
doaj   +1 more source

Optimalisasi penjadwalan lampu lalu lintas adaptif berbasis Proximal Policy Optimization (PPO) dan Vanet di persimpangan

Kemacetan lalu lintas pada persimpangan jalan perkotaan dengan volume kendaraan tinggi, seperti Simpang Samsat di Kota Bandung, memerlukan sistem pengendalian yang lebih adaptif dibandingkan sistem waktu tetap (fixed-time) konvensional.
Yandiyan, Yandiyan
core   +1 more source

Investigating the Impact of Curriculum Learning on Reinforcement Learning for Improved Navigational Capabilities in Mobile Robots

Inteligencia Artificial
This paper proposes a method for finding the shortest path of a mobile robot using deep reinforcement learning with utilizing Proximal policy optimization algorithm (PPO) enhanced with curriculum learning.
Alaa Iskandar, Béla Kovács
doaj  

A dynamical clipping approach with task feedback for Proximal Policy Optimization

Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the performance of PPO.
Xu, Jingzehua, Liu, Jinxin, Zhang, Ziqi, Zhuang, Zifeng, wang, Donglin, Zhang, Hongyin, Zhang, Shuai   +6 more
core