Multi-agent reinforcement learning

Results 191 to 200 of about 139,994 (273)

Too Complex to Choose? The Role of Heuristics in Shaping Farmers' Willingness to Pay for Income Stabilization Tool in Italy

Agribusiness, EarlyView.
ABSTRACT European agriculture is increasingly exposed to economic instability driven by extreme weather events, market volatility, and geopolitical tensions. To manage these growing risks, farmers are encouraged to adopt innovative risk management strategies such as the Income Stabilization Tool (IST), which offers protection against severe income ...
Alice Stiletto +5 more
wiley +1 more source

A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities. [PDF]

Sci Rep
Adjei PK +7 more
europepmc +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley +1 more source

Multi-agent reinforcement learning algorithms. [PDF]

, 2011
openaire +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir +4 more
wiley +1 more source

Author Correction: A multi-agent reinforcement learning based approach for automatic filter pruning. [PDF]

Sci Rep
Li Z, Zuo X, Song Y, Liang D, Xie Z.
europepmc +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen +5 more
wiley +1 more source

Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks. [PDF]

Sci Rep
Prabha KL, Mengash HA, Alqahtani H, Allafi R. +3 more
europepmc +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen +3 more
wiley +1 more source

reinforcement learning
deep reinforcement learning
multi-agent system

multi-agent systems