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Hierarchical Method for Cooperative Multiagent Reinforcement Learning in Markov Decision Processes
AbstractIn the rapidly evolving field of reinforcement learning, combination of hierarchical and multiagent learning methods presents unique...
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Enhancing cut selection through reinforcement learning
With the rapid development of artificial intelligence in recent years, applying various learning techniques to solve mixed-integer linear programming...
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Edge Dismantling with Geometric Reinforcement Learning
The robustness of networks plays a crucial role in various applications. Network dismantling, the process of strategically removing nodes or edges to...
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Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search
Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine...
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Scheduling in Multiagent Systems Using Reinforcement Learning
AbstractThe paper is devoted to scheduling in multiagent systems in the framework of the Flatland 3 competition. The main aim of this competition is...
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A deep reinforcement learning framework for solving two-stage stochastic programs
In this study, we present a deep reinforcement learning framework for solving scenario-based two-stage stochastic programming problems. Stochastic...
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Optimal pivot path of the simplex method for linear programming based on reinforcement learning
Based on the existing pivot rules, the simplex method for linear programming is not polynomial in the worst case. Therefore, the optimal pivot of the...
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An Intelligent Choice of Witnesses in the Miller–Rabin Primality Test. Reinforcement Learning Approach
AbstractThe problem of testing natural numbers for primality is an important problem for the Theory of Numbers and Cryptography. The main instrument...
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A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack
In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement...
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Reinforcement learning of simplex pivot rules: a proof of concept
At each iteration of the simplex method there are typically many possible entering columns. We use deep value-based reinforcement learning to choose...
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Deficient RC Slabs Strengthened with Combined FRP Layer and High-Performance Fiber-Reinforced Cementitious Composite
Nowadays, the strengthening of concrete structures to withstand excessive loads and increase the structure’s ductility, etc., using high-performance...
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Active control of flow past an elliptic cylinder using an artificial neural network trained by deep reinforcement learning
The active control of flow past an elliptical cylinder using the deep reinforcement learning (DRL) method is conducted. The axis ratio of the...
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A Sojourn-Based Approach to Semi-Markov Reinforcement Learning
In this paper we introduce a new approach to discrete-time semi-Markov decision processes based on the sojourn time process. Different...
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Reinforcement learning and stochastic optimisation
At the heart of financial mathematics lie stochastic optimisation problems. Traditional approaches to solving such problems, while applicable to...
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Job Shop Scheduling via Deep Reinforcement Learning: A Sequence to Sequence Approach
Job scheduling is a well-known Combinatorial Optimization problem with endless applications. Well planned schedules bring many benefits in the...
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Resource Allocation in 5G and Beyond Edge-Slice Networking Using Deep Reinforcement Learning
5G Networks and Multi-access Edge Computing (MEC) will serve various use cases of emerging technologies with a wide range of requirements of multiple...
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Nonlinear Optimal Control Using Deep Reinforcement Learning
We propose a shift of paradigm for the control of fluid flows based on the application of deep reinforcement learning (DRL). This strategy is quickly...
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Approximate boundary conditions for a Mindlin–Timoshenko plate surrounded by a thin layer
We consider the model of Mindlin–Timoshenko for a multi-structure composed of an elastic plate surrounded by a thin layer of uniform thickness. From...
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Improving the efficiency of reinforcement learning for a spacecraft powered descent with Q-learning
Reinforcement learning entails many intuitive and useful approaches to solving various problems. Its main premise is to learn how to complete tasks...
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Relational Graph Attention-Based Deep Reinforcement Learning: An Application to Flexible Job Shop Scheduling with Sequence-Dependent Setup Times
This paper tackles a manufacturing scheduling problem using an Edge Guided Relational Graph Attention-based Deep Reinforcement Learning approach....