Results 231 to 240 of about 137,924 (252)

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Advanced Intelligent Systems, EarlyView.
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari, Teresa Serrano‐Gotarredona, Bernabé Linares‐Barranco   +2 more
wiley   +1 more source

Multivariate Contrastive Predictive Coding with Sliding Windows for Disease Prediction from Electronic Health Records

Advanced Intelligent Systems, EarlyView.
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan, Xiaozhu Jing, Yuzheng Yan, Wuman Luo   +3 more
wiley   +1 more source

A Multimodal Laser‐Induced Graphene‐Based Flexible Sensor for Soft Robotic Hand Environmental Perception

Advanced Intelligent Systems, EarlyView.
A multimodal laser‐induced graphene (LIG)‐based flexible sensor is developed to detect proximity and contact signals. Integrated into a soft robotic hand, it enables vision‐free object searching and grasping. Combined with a convolutional neural network, the system achieves accurate material and texture recognition, enhancing the capability of ...
Youning Duo, Jinxi Duan, Xinyu Hao, Kaining Tao, Xin Liu, Mingyuan Li, Xingyu Chen, Wenbo Liu, Zonghao Zuo, Li Wen   +9 more
wiley   +1 more source

Multi-agent reinforcement learning algorithms

, 2011
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Multi-Agent Reinforcement Learning

2020
In reinforcement learning, complicated applications require involving multiple agents to handle different kinds of tasks simultaneously. However, increasing the number of agents brings in the challenges on managing the interactions among them. In this chapter, according to the optimization problem for each agent, equilibrium concepts are put forward to
Huaqing Zhang, Shanghang Zhang
openaire   +3 more sources

Multi-Agent Inverse Reinforcement Learning

2010 Ninth International Conference on Machine Learning and Applications, 2010
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multi-agent inverse reinforcement learning, where reward functions of multiple agents are learned by observing their uncoordinated ...
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude Shavlik   +5 more
openaire   +1 more source

Hierarchical multi-agent reinforcement learning

Autonomous Agents and Multi-Agent Systems, 2001
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL.
Ghavamzadeh, M, Mahadevan, S, Makar, R
openaire   +1 more source

Multi-agent reinforcement learning

2016 13th Symposium on Neural Networks and Applications (NEUREL), 2016
Reinforcement learning deals with the problem of how to map situations (states) to actions so as to maximize a numerical reward while interacting with dynamical and uncertain environment. Within the framework of Markov Decision Processes (MDPs) these methods are typically based on approximate dynamic programming using appropriate calculation ...
openaire   +1 more source

Multi-agent reinforcement learning: weighting and partitioning

Neural Networks, 1999
This article addresses weighting and partitioning, in complex reinforcement learning tasks, with the aim of facilitating learning. The article presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with differential weighting in these regions, to exploit differential ...
R, Sun, T, Peterson
openaire   +2 more sources

AnthillRL: Multi-agent Reinforcement Learning

Anais do IX Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações (WESAAC 2015), 2015
Ants are essentially social insects, and their organizational depen dency reflects directly on their survival. Due to their social nature, ant society provides a rich model for analysing properties of multiagent systems such as col laboration and effectiveness of collective action.
Anibal Sólon Heinsfeld, Felipe Meneguzzi   +1 more
openaire   +1 more source