Results 31 to 40 of about 6,304,901 (198)

ExO-PPO: an Extended Off-policy Proximal Policy Optimization Algorithm

CoRR
Deep reinforcement learning has been able to solve various tasks successfully, however, due to the construction of policy gradient and training dynamics, tuning deep reinforcement learning models remains challenging. As one of the most successful deep reinforcement-learning algorithm, the Proximal Policy Optimization algorithm (PPO) clips the policy ...
Hanyong Wang, Menglong Yang
openaire   +3 more sources

CAPO: A Coherence-Adaptive Advantage Mechanism Instantiated in Proximal Policy Optimization

Applied Sciences
Performance degradation caused by temporal-difference signal fluctuations and the inflexibility of fixed proximal constraints remains a key challenge in policy optimization algorithms.
Mingjie Li, Jiajie Hu, Weijun Wang, Tao Zhang, Daoshun Xie, Zhihui Hu, Jiangfeng Xian   +6 more
doaj   +1 more source

Energy-Efficient Transmission Strategy for Delay Tolerable Services in NOMA-Based Downlink With Two Users

IEEE Access, 2023
With the continuous development of the communication industry, there is a shift in real-time services from 4G networks to Delay Tolerable (DT) services in the context of 5G/B5G networks.
Mengmeng Bai, Rui Zhu, Jianxin Guo, Feng Wang, Liping Wang, Hangjie Zhu, Lei Huang, Yushuai Zhang   +7 more
doaj   +1 more source

The 37 Implementation Details of Proximal Policy Optimization

, 2022
Proximal policy optimization (PPO) has become one of the most popular deep reinforcement learning (DRL) algorithms. Yet, reproducing the PPO's results has been challenging in the community.
Raffin, Antonin, Dossa, Rousslan Fernand Julien, Wang, Weixun, Kanervisto, Anssi, Huang, Shengyi   +4 more
core  

A dual-arm cooperative control method based on improved proximal policy optimization

Journal of King Saud University: Computer and Information Sciences
To address the challenges of high-dimensional control in dual-arm collaborative tasks, the complexity of multi-stage decision-making, and the limitations of traditional Proximal Policy Optimization (PPO) algorithms due to their single constraint ...
Man Su, Qingni Yuan, Pengju Qu, Chao Wang, Yinjiang Zhou   +4 more
doaj   +1 more source

Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots

Advanced Robotics Research, EarlyView.
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou, Zhiqiang Chen, Yuanbiao Ma, Zhiqiang Tang, Qianqian Wang   +4 more
wiley   +1 more source

DRIVE‐SAFE: Data‐Driven Robustness and Informed Validation for Evolving Specifications via Formal Evaluation

Advanced Robotics Research, EarlyView.
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu   +3 more
wiley   +1 more source

CIM-PPO:Proximal Policy Optimization with Liu-Correntropy Induced Metric

CoRR, 2021
This work has been submitted to the Elsevier for possible ...
Yunxiao Guo, Han Long, Xiaojun Duan, Kaiyuan Feng, Maochu Li, Xiaying Ma   +5 more
openaire   +2 more sources

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source