Results 91 to 100 of about 6,304,901 (198)

Method for optimizing and improving communication petformance of distributed power service based on 5G NR-U

Dianzi Jishu Yingyong
With the extensive access of distributed power sources to the power grid, 5G licensed spectrum resources are becoming more and more scarce. It has become an important research hotspot to establish power radio network by using 5G new radio in unlicensed ...
Jiang Chengling, Lu Min, Yang Linqing, Shi Xiaowei   +3 more
doaj   +1 more source

A low-carbon optimization scheduling method of CIES based on PPO algorithm

电力工程技术
The tiered carbon trading mechanism and optimization scheduling model solving algorithm are pivotal for the community integrated energy system (CIES).
CHEN Fan, WU Lingxiao, WANG Man, LYU Ganyun, ZHANG Xiaolian   +4 more
doaj   +1 more source

Hybrid intelligent optimization strategy of battery swapping station for electric vehicles based on reinforcement learning

Computational Urban Science
Smart transportation is an important application scenario in the field of urban computing. As the popularity of electric vehicles increases, the demand for fast charging is growing rapidly.
Hongwei Yi, Huan Zhang, Jianghong Li, Yanling Zhao   +3 more
doaj   +1 more source

Proximal policy optimization-based reinforcement learning approach for DC-DC boost converter control: A comparative evaluation against traditional control techniques

Heliyon
This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods.
Utsab Saha, Atik Jawad, Shakib Shahria, A.B.M Harun-Ur Rashid   +3 more
doaj   +1 more source

Koopman-Inspired Proximal Policy Optimization (KIPPO) [PDF]

Reinforcement Learning (RL) has made significant strides in various domains, yet developing effective control policies for environments with complex, nonlinear dynamics remains a challenge, particularly for policy gradient methods.
Cozma, Andrei
core  

Real-World Autonomous Driving Control: An Empirical Study Using the Proximal Policy Optimization (PPO) Algorithm

Evergreen
This article preprocesses environmental information and use it as input for the Proximal Policy Optimization (PPO) algorithm. The algorithm is directly trained on a model vehicle in a real environment, allowing it to control the distance between the vehicle and surrounding objects.
Zhao, Peng, Yuan, Zhongxian, Thu, Kyaw, Miyazaki, Takahiko   +3 more
openaire   +3 more sources

Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization

Sensors
This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement ...
Ching-Chang Wong, Tai-Ting Tsai, Can-Kun Ou   +2 more
doaj   +1 more source

Beyond the Boundaries of Proximal Policy Optimization

Proximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed into the inner-loop estimation of update vectors, and the outer-loop ...
Huszár, Ferenc, Foerster, Jakob N., Tan, Charlie B., Toledo, Edan, Ellis, Benjamin   +4 more
core  

PPO-EAL: Exact Augmented Lagrangian Proximal Policy Optimization for Safe Robotic Control

Reinforcement learning (RL) has emerged as a promising solution to accomplish complex robotic control tasks; however, most of the current work ignores the safety requirements. Safe RL seeks to maximize task performance while satisfying explicit physical constraints, but current algorithms struggle to learn the policy efficiently with precise constraint
Ding, Jiatao, Gao, Songqun, Del Prete, Andrea, Saveriano, Matteo   +3 more
openaire   +2 more sources

Proximal Policy Optimization with Adaptive Exploration [PDF]

Proximal Policy Optimization with Adaptive Exploration (axPPO) is introduced as a novel learning algorithm. This paper investigates the exploration-exploitation tradeoff within the context of reinforcement learning and aims to contribute new insights ...
Lixandru, Andrei
core   +1 more source