23 papers · ranked by Valyu relevance
Gorker Alp Malazgirt, Osman Ünsal, Adrián Cristal Kestelman
In this paper, we propose TauRieL and target Traveling Salesman Problem (TSP) since it has broad applicability in theoretical and applied sciences. TauRieL utilizes an actor-critic inspired architecture that adopts ordinary feedforward nets to obtain a policy update vector v. Then, we use v to improve the state…
Anh Vu Le, Prabakaran Veerajagadheswar, Phone Thiha Kyaw, Mohan Rajesh Elara + 2 more
'Mohan Rajesh Elara' 'Nguyen Huu Khanh Nhan' 'Gregor Klancar'] One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative…
Zhang-Hua Fu, Kai-Bin Qiu, Hongyuan Zha
For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of…
Paulo Roberto de Oliveira da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akçay
'Alp Akçay'] Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art…
Jin Zhang, Qing Liu, XiaoHang Han, Shih-Wei Lin
In this paper, a dynamic sub-route-based self-adaptive beam search Q-learning (DSRABSQL) algorithm is proposed that provides a reinforcement learning (RL) framework combined with local search to solve the traveling salesman problem (TSP). DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of…
Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He + 4 more
'Lei Song' 'Jiang Bian'] Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology. Recently, Deep Reinforcement Learning (DRL) has been increasingly…
Yunhao Yang, Andrew B. Whinston
This paper gives a detailed review of reinforcement learning in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1960s, and compares it with the reinforcement learning algorithms in recent years. We explicitly look at a famous combinatorial problem known as the Traveling…
Yong Liang Goh, Wee Sun Lee, Xavier Bresson, Laurent Thomas + 1 more
'Nicholas Lim'] The traveling salesman problem is a fundamental combinatorial optimization problem with strong exact algorithms. However, as problems scale up, these exact algorithms fail to provide a solution in a reasonable time. To resolve this, current works look at utilizing deep learning to construct reasonable…
Won-Jun Kim, Junho Jeong, Taeyeong Kim, Kichun Lee + 1 more
Deep reinforcement learning (DRL) as a routing problem solver has shown promising results in recent studies. However, an inherent gap exists between computationally driven DRL and optimization-based heuristics. While a DRL algorithm for a certain problem is able to solve several similar problem instances, traditional…
Abhishek Nayak, Sivakumar Rathinam, Hriday Bavle, Jose Luis Sanchez-Lopez + 1 more
'Jose Luis Sanchez-Lopez' 'Holger Voos'] This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as…
Ahmed Stohy, Heba-Tullah Abdelhakam, Sayed Ali, Mohammed Elhenawy + 5 more
'Abdallah A. Hassan' 'Mahmoud Masoud' 'Sebastien Glaser' 'Andry Rakotonirainy' 'Seyedali Mirjalili'] In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an…
Chun-Sheng Zhao, Li-Pei Wong, Yangming Zhou
Leveraging the Transformer architecture to develop end-to-end models for addressing combinatorial optimization problems (COPs) has shown significant potential due to its exceptional performance. Nevertheless, a multitude of COPs, including the Traveling Salesman Problem (TSP), displays typical graph structure…
Elena Zamaraeva, Christopher M. Collins, Dmytro Antypov, Vladimir V. Gusev + 6 more
Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to…
Pranav Mahajan, Ben Seymour
The seminal reward prediction error theory of dopamine function faces several key challenges. Most notable is the difficulty learning multiple rewards simultaneously, inefficient on-policy learning, and accounting for heterogeneous striatal responses in the tail of the striatum. We propose a normative framework, based…
Minsu Abel Yang, Jee Hang Lee, Sang Wan Lee
Recent advances in reinforcement learning (RL) have successfully addressed several challenges, such as performance, scalability, or sample efficiency associated with the use of this technology. Although RL algorithms bear relevance to psychology and neuroscience in a broader context, they lack biological plausibility.…
Gautam Reddy
Problem-solving and reasoning involve mental exploration and navigation in sparse relational spaces. A physical analogue is spatial navigation in structured environments such as a network of burrows. Recent experiments with mice navigating a labyrinth show a sharp discontinuity during learning, corresponding to a…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Pranav Mahajan, Ben Seymour
The seminal reward prediction error account of dopamine has been highly successful, but faces several key challenges. Most notable are the difficulty of learning multiple rewards simultaneously, inefficient on-policy learning, and accounting for the heterogeneous striatal responses observed across and within striatal…
Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe + 2 more
Deep Reinforcement Learning is a branch of artificial intelligence that uses artificial neural networks to model reward-based learning as it occurs in biological agents. Here we modify a Deep Reinforcement Learning approach by imposing a suppressive effect on the connections between neurons in the artificial network -…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
Authors not listed
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were…