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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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Deep reinforcement learning for approximate policy iteration: convergence analysis and a post-earthquake disaster response case study
Approximate policy iteration (API) is a class of reinforcement learning (RL) algorithms that seek to solve the long-run discounted reward Markov...
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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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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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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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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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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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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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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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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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Mathematical Intuition, Deep Learning, and Robbins’ Problem
The present article is an essay about mathematical intuition and Artificial intelligence (A.I.), followed by a guided excursion to Robbins’ Problem...
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Deep Learning for Natural Language Processing: A Survey
Over the last decade, deep learning has revolutionized machine learning. Neural network architectures have become the method of choice for many...
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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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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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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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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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Explaining the Behavior of Reinforcement Learning Agents Using Association Rules
Deep reinforcement learning algorithms are increasingly used to drive decision-making systems. However, there exists a known tension between the...
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Multi-agent Reinforcement Learning Aided Sampling Algorithms for a Class of Multiscale Inverse Problems
In this work, we formulate a class of multiscale inverse problems within the framework of reinforcement learning (RL) and solve it by a sampling...
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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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Controlling a Dynamic System Through Reinforcement Learning
Control theory deals with the problem of finding a control law for a given dynamic system on which one can act by means of a command. The goal is...