Results 51 to 60 of about 10,350,910 (281)
, 2023 Reinforcement learning, especially deep reinforcement learning, has made many advances in the last decade. Similarly, great strides have been made in multi-agent reinforcement learning. Systems of cooperative autonomous robots are increasingly being used,
de Gooijer, Jessica (author)
core
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Multi-agent Reinforcement Learning Algorithm Based on AI Planning [PDF]
Jisuanji kexueAt present,deep reinforcement learning algorithms have made a lot of achievements in various fields.However,in the field of multi-agent task,agents are often faced with non-stationary environment with larger state-action space and sparse rewards,low ...
XIN Yuanxia, HUA Daoyang, ZHANG Li
doaj +1 more source
A Cooperative Multi-Agent Reinforcement Learning Method Based on Coordination Degree
IEEE Access, 2021 Multi-agent reinforcement learning (MARL) has become a prevalent method for solving cooperative problems owing to its tractable implementation and task distribution. The goal of the MARL algorithms for fully cooperative scenarios is to obtain the optimal
Haoyan Cui, Zhen Zhang
doaj +1 more source
Advanced Engineering Materials, EarlyView.The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ +19 more
wiley +1 more source
Dual Preference Learning for Multi-Agent Reinforcement Learning
IEEE AccessDesigning effective reward functions is fundamental challenging in reinforcement learning, especially in complex multi-agent systems with intricate credit assignment.
Sehyeok Kang +3 more
doaj +1 more source
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During the centralized training, agents can be guided by the same signals, such as the global state. However, agents lack the shared signal and choose actions given local observations during execution.
Zhiwei Xu 0005 +6 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
An integrated trust and reputation model for open multi-agent systems
, 2006 Trust and reputation are central to effective interactions in open multi-agent systems in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system.
Jennings, N. R. +5 more
core +1 more source
Advances in Sustainable and Wearable Textile Based Soft Robotics
Advanced Functional Materials, EarlyView.This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas +6 more
wiley +1 more source