Results 91 to 100 of about 184,437 (258)
Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs
Advanced Intelligent Systems, EarlyView.This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza +2 more
wiley +1 more source
Comparing Labelled Markov Decision Processes
, 2020 35 pages, 7 ...
Kiefer, Stefan, Tang, Qiyi
openaire +4 more sources
Enabling Stochastic Dynamic Games for Robotic Swarms
Advanced Intelligent Systems, EarlyView.This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
Control Design for Untimed Petri Nets Using Markov Decision Processes
Operations Research and Decisions, 2017 Design of control sequences for discrete event systems (DESs) has been presented modelled by untimed Petri nets (PNs). PNs are well-known mathematical and graphical models that are widely used to describe distributed DESs, including choices ...
Cherki Daoui, Dimitri Lefebvre
doaj
Advanced Intelligent Systems, EarlyView.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Markov Decision Processes [PDF]
Journal of the Royal Statistical Society. Series A (Statistics in Society), 1994 J. Q. Smith, D. J. White
openaire +2 more sources
Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption. [PDF]
FME, 2021 Blahoudek F +5 more
europepmc +1 more source
Nonparametric Identification of Behavioral Responses to Counterfactual Policy Interventions in Dynamic Discrete Decision Processes [PDF]
This paper deals with identification in Markov dynamic discrete decision processes. It shows the nonparametric identification of the behavioral responses to counterfactual policy interventions that modify the one- period utility function.Dynamic discrete
Victor Aguirregabiria
core
Adaptive Macroscopic Ensemble Allocation for Robot Teams Monitoring Spatiotemporal Processes
Advanced Intelligent Systems, EarlyView.We propose an online, environment feedback‐driven macroscopic ensemble approach to adapt robot team task allocation in spatiotemporal environments by controlling robot populations rather than assigning individual robots, all while maintaining robust team performance even for small teams. Our simulation and experimental results show better or comparable
Victoria Edwards +2 more
wiley +1 more source