Results 31 to 40 of about 6,497 (242)

Approximate receding horizon approach for Markov decision processes: average reward case

Journal of Mathematical Analysis and Applications, 2003
The authors consider an approximation scheme for solving Markov decision processes (MDPs) with countable state space, finite action space, and bounded rewards that uses an approximate solution of a fixed finite-horizon sub-MDP of a given infinite-horizon MDP to create a stationary policy, which they call ''approximate receding horizon control''.
Chang, Hyeong Soo, Marcus, Steven I.
openaire   +2 more sources

Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice

Paladyn, 2018
Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). IRL extracts an unknown reward function from samples of observed behaviour. Traditional IRL systems require large datasets to
Bhattacharyya Rupam, Hazarika Shyamanta M.   +1 more
doaj   +1 more source

A State Aggregation Approach To Singularly Perturbed Markov Reward Processes

, 2008
In this paper, we propose a single sample path based algorithm with state aggregation to optimize the average rewards of singularly perturbed Markov reward processes (SPMRPs) with a large scale state spaces. It is assumed that such a reward process depend on a set of parameters.
Zhang, Dali, Baoqun Yin, Hongsheng Xi
openaire   +2 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

Advanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao, Hongzhou Ai, Wenjun Xu, Zude Zhou, Lihui Wang   +4 more
wiley   +1 more source

Optimizing Subway Train Operation With Hierarchical Adaptive Control Approach

IEEE Access, 2023
The proportional integral derivative (PID) method is widely used in industrial control applications. However, when applied to complex and dynamic train operation control systems, real-time parameter adjustment becomes a formidable challenge.
Gaoyun Cheng, Dianyuan Wang, Ming Sun, Zhe Fu, Binbin Yuan, Lei Zhang, Xiao Xiao   +6 more
doaj   +1 more source

Single‐Cell Dissection of Therapy‐Induced Remodeling Uncovers a Fibroblast‐Driven Immunosuppressive Niche and Targetable Vulnerabilities in Lethal Prostate Cancer

Advanced Science, EarlyView.
Single‐cell longitudinal profiling reveals that androgen‐deprivation therapy induces a DPT+ fibroblast‐complement axis that suppresses macrophage inflammation and drives CD8+ T cell exhaustion in prostate cancer. Concurrently, resistant epithelial subpopulations persist and engage TSPAN1‐ and NRXN1‐mediated programs promoting CRPC and neuroendocrine ...
Yang Chen, Dandan Dong, Jinling Liao, Naikai Liao, Mengqi Liu, Qin Zhang, Siting Chen, Qi Liu, Ying Lu, Tianyu Li, Chunlin Zou, Qiong Song, Qiuyan Wang, Jian Liang, Qiji Xie, Chengyang Li, Mengyun Wang, Jiwen Cheng, Zengnan Mo, Gong‐Hong Wei   +19 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies

Network
Sequential decision-making in dynamic and interconnected environments is a cornerstone of numerous applications, ranging from communication networks and finance to distributed blockchain systems and IoT frameworks. The multi-armed bandit (MAB) problem is
Abdalaziz Sawwan, Jie Wu
doaj   +1 more source

Asymptotic Optimality and Rates of Convergence of Quantized Stationary Policies in Continuous-Time Markov Decision Processes

Discrete Dynamics in Nature and Society, 2022
This paper is concerned with the asymptotic optimality of quantized stationary policies for continuous-time Markov decision processes (CTMDPs) in Polish spaces with state-dependent discount factors, where the transition rates and reward rates are allowed
Xiao Wu, Yanqiu Tang
doaj   +1 more source