Results 71 to 80 of about 6,304,901 (198)

A Hybrid Physics‐Informed Computational Framework for Adaptive Microrobotic Navigation and Causal Structure Discovery in Simulated Non‐Newtonian Environments

Applied Research, Volume 5, Issue 5, October 2026.
This study presents a hybrid AI framework that enables autonomous magnetic microrobots to navigate complex, non‐Newtonian tumor microenvironments. By integrating physics‐informed recurrent reinforcement learning with active persistent excitation, the system successfully infers hidden fluid rheology for precise targeted navigation and structural causal ...
Xinyuan Chen, Zanariah Abu Bakar, Mohd Nizam Husen   +2 more
wiley   +1 more source

Artificial Intelligence in Invisible Orthodontics: From Digital Impressions to Adaptive Treatment Management

Engineering Reports, Volume 8, Issue 10, October 2026.
Digital transformation in orthodontics has significantly revolutionized clinical practices over recent decades. Machine learning algorithms, particularly convolutional neural networks (CNNs) and artificial neural networks (ANNs), offer superior performance in tasks such as identifying anatomical landmarks on radiographic images, assessing skeletal ...
Soodeh Hosseini, Hossein Seilani, At‐har Nasari   +2 more
wiley   +1 more source

Dynamic path planning of autonomous mobile robot in off-road environments using experience replay enhanced distributed proximal policy optimization algorithm

Geo-spatial Information Science
In off-road environments, some problems like insufficient consideration of environmental trafficability, difficulty in model convergence, and limited generalization often exist in autonomous mobile robot (AMR) path planning research based on ...
Qingyun Liu, Xiong You, Xin Zhang, Jiwei Zuo, Jia Li   +4 more
doaj   +1 more source

Deep Reinforcement Learning Based Autonomous Decision‐Making for Cooperative Uncrewed Aerial Vehicles: A Search and Rescue Real World Application

Journal of Field Robotics, Volume 43, Issue 7, Page 4382-4401, October 2026.
ABSTRACT This paper presents the first end‐to‐end framework that combines guidance, navigation, and centralized task allocation for multiple UAVs performing autonomous search‐and‐rescue (SAR) in GNSS‐denied indoor environments. A twin delayed deep deterministic policy gradient controller is trained with an artificial potential field (APF) reward that ...
Thomas Hickling, Maxwell Hogan, Abdulla Tammam, Nabil Aouf   +3 more
wiley   +1 more source

A planar tracking strategy based on multiple-interpretable improved PPO algorithm with few-shot technique

Scientific Reports
Facing to a planar tracking problem, a multiple-interpretable improved Proximal Policy Optimization (PPO) algorithm with few-shot technique is proposed, namely F-GBQ-PPO.
Xiao Wang, Zhe Ma, Lu Cao, Dechao Ran, Mingjiang Ji, Kewu Sun, Yuying Han, Jiake Li   +7 more
doaj   +1 more source

Multi-Agent PPO-Based Resource Optimization for Full-Duplex RIS-Aided NOMA-ISAC Systems

IEEE Open Journal of the Communications Society
This paper proposes a multi-agent deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO) for joint resource optimization in full-duplex (FD) reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access ...
Nonis Wara, Anal Paul, Keshav Singh, Aryan Kaushik, Wonjae Shin   +4 more
doaj   +1 more source

Behavior Proximal Policy Optimization

, 2023
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
Liu, Jinxin, Zhuang, Zifeng, Lei, Kun, Wang, Donglin, Guo, Yilang   +4 more
core  

Guest Editorial: Computational Intelligence in Dynamic and Uncertain Environments

CAAI Transactions on Intelligence Technology, EarlyView.
Shouyong Jiang
wiley   +1 more source

A proximal policy optimization based deep reinforcement learning framework for tracking control of a flexible robotic manipulator

Results in Engineering
This paper puts forward a policy feedback based deep reinforcement learning (DRL) control scheme for a partially observable system by leveraging the potentials of proximal policy optimization (PPO) algorithm and convolutional neural network (CNN ...
Joshi Kumar V, Vinodh Kumar Elumalai
doaj   +1 more source

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too ...
Duong Hien Chi Kien
core   +1 more source