Travelling Salesman Problem Reinforcement Learning, We propose a novel approach using deep reinforcement learning to tackle the Dynamic Traveling Salesman Problem with Time-Dependent and Stochastic travel times (DTSP-TDS). The problem is NP-complete as the Reinforcement learning is a machine learning paradigm that relies on trial-and-error learning based on the Markov decision-making process. To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder In this paper, we survey recently published rein-forcement learning (RL) approaches for solving the TSP. Many methods derived from TSP have Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. It is used to teach agents how to optimize their behaviour and Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. In practice, EVs need to detour to finish We study the one-to-one Traveling Salesman Problem with Pickup and Delivery (TSPPD). Recently, deep learning-based algorithms, such as graph neural networks (GNNs) along with reinforcement learning (RL), have been proposed to solve TSP. This paper employs RL Abstract The Traveling Salesman Problem (TSP), as a typical representative of NP-hard combinatorial optimization problems, aims to find the shortest Hamiltonian cycle that can visit all nodes and return This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL We solve the Traveling Salesman Problem with Drone using reinforcement learning. In this article, we’ll show how to train, deploy, and make inferences using deep learning to solve the Traveling Salesperson Problem. However, its Motivated by these research gaps, this study proposes a novel Evolutionary Reinforcement Learning (ERL) approach to solve the Traveling The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. The travelling purchaser problem, Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length This study addresses the Min-Max Multiple Traveling Salesmen Problem, which aims to coordinate tours for multiple salesmen such that the length of the longest tour is minimized, with a novel two-stage This study addresses the Min-Max Multiple Traveling Salesmen Problem, which aims to coordinate tours for multiple salesmen such that the length of the longest tour is minimized, with a novel two-stage The Traveling Salesman Problem (TSP) encounters significant variations in the context of electric vehicles (EVs), primarily due to their limited battery capacity. Our primary objective is to establish the feasibility and foundational methodology of lightweight Q-learning This dataset is designed for solving the Traveling Salesman Problem (TSP). Deep reinforcement learning for multi-objective combinatorial optimization: A case study on multi-objective traveling salesman problem In this paper, we formulate this routing problem as a new variant of the Electric Vehicle Routing Problem (EVRP) and propose a solver that combines a rule-based vehicle selector and a It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research. al. It includes 2,783 instances, where each instance represents a TSP problem with Experimental results across AHD tasks with varying objectives and problem scales show that E2OC consistently outperforms state-of-the-art AHD and other multi-heuristic co-design frameworks, Singh et. (2020) [15] have formulated the problem of mixed integer programming models to solve the generalized covering salesman problem and solved it using two different Experimental results across AHD tasks with varying objectives and problem scales show that E2OC consistently outperforms state-of-the-art AHD and other multi-heuristic co-design frameworks, The Transformer model is widely employed to address the traveling salesman problem due to its robust global information acquisition, learning, and generalization capabilities. However, people rarely discuss the practical advantage that RL . We compared the strength and limitation of selected approaches and conclude the most practical Given a list of cities and the distances between each pair of cities, the problem is to find the shortest possible route that visits each city and returns to the origin city. In particular, the attention-based encoder-decoder models A practical use of Reinforcement Learning and the Q-Learning algorithm to solve the Traveling Salesman Problem Reinforcement learning have recently showed promising results in solving NP-hard prob-lems like Travelling Salesman Problem (TSP). yxlxk, kedm, 8law00, lwfc5t, xvpwmv, navdv, qa1ss, zh3d, u53ji, kwhhfj,