Results 71 to 80 of about 212,805 (267)
Science and Technology for Energy TransitionServerless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications.
Kaur Jasmine, Chana Inderveer, Bala Anju
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A Reinforcement Learning Approach to Determine When and How Many Stocks to Buy in Stock Trading [PDF]
پژوهشهای نظری و کاربردی هوش ماشینیDue to the volatility and uncertainty inherent in the stock market, devising an optimal trading strategy is a complex endeavor. Given the non-repetitive nature of trading circumstances, learning through interactions becomes imperative.
ولی درهمی, Fatemeh Darezereshki
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, 2011 We introduce a new convergent variant of Q-learning, called speedy Q-learning, to address the problem of slow convergence in the standard form of the Q-learning algorithm. We prove a PAC bound on the performance of SQL, which shows that for an MDP with n state-action pairs and the discount factor γ only T = O(log(n)/(ε^2 (1 - γ)^4)) steps are required ...
Azar, M.G. +3 more
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A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
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Complex System Modeling and SimulationThe lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan.
Ping Wang, Renato De Leone, Hongyan Sang
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Combining Q-Learning and the Hungarian Algorithm for Multi-Robot Systems in Dynamic Environments [PDF]
E3S Web of ConferencesThis paper presents a hierarchical reinforcement learning framework for multi-robot systems in dynamic warehouse environments. The proposed approach integrates Q-learning at two levels: motion control and task reassignment.
Nguyen Hoang Mai +4 more
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What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Complex System Modeling and SimulationThis work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption.
Benxue Lu +3 more
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A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
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A Reinforcement Learning Approach for Smart Farming [PDF]
Database Systems Journal, 2019 At a basic level, the aim of machine learning is to develop solutions for real-life engineering problems and to enhance the performance of different computers tasks in order to obtain an algorithm that is highly independent of human intervention.
Gabriela ENE
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