On Monte Carlo Tree Search for Weighted Vertex Coloring

• URL: http://arxiv.org/abs/2202.01665v1
• Date: Thu, 3 Feb 2022 16:27:55 GMT
• Title: On Monte Carlo Tree Search for Weighted Vertex Coloring
• Authors: Cyril Grelier, Olivier Goudet and Jin-Kao Hao
• Abstract summary: This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicateds for solving the Weighted Vertex Coloring Problem.
• Score: 15.308312172985486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess the value of each studied combination. We also provide empirical evidence to shed light on the advantages and limits of each strategy.

Related papers

• Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment [0.0]
  Monte Carlo Tree Search (MCTS) is a powerful algorithm for solving complex decision-making problems. This paper presents an optimized MCTS implementation applied to the FrozenLake environment, a classic reinforcement learning task.
  arXiv  Detail & Related papers  (2024-09-25T05:04:53Z)
• LiteSearch: Efficacious Tree Search for LLM [70.29796112457662]
  This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
  arXiv  Detail & Related papers  (2024-06-29T05:14:04Z)
• An Optimal Algorithm for the Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit [65.268245109828]
  We study the real-valued pure exploration problem in the multi-armed bandit (R-CPE-MAB) Existing methods in the R-CPE-MAB can be seen as a special case of the so-called transductive linear bandits. We propose an algorithm named the gap-based exploration (CombGapE) algorithm, whose sample complexity matches the lower bound.
  arXiv  Detail & Related papers  (2023-06-15T15:37:31Z)
• Combining Monte Carlo Tree Search and Heuristic Search for Weighted Vertex Coloring [15.308312172985486]
  This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicateds for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several variants where conventional random simulation is replaced by other simulation strategies. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants.
  arXiv  Detail & Related papers  (2023-04-24T14:50:33Z)
• Continuous Monte Carlo Graph Search [61.11769232283621]
  Continuous Monte Carlo Graph Search ( CMCGS) is an extension of Monte Carlo Tree Search (MCTS) to online planning. CMCGS takes advantage of the insight that, during planning, sharing the same action policy between several states can yield high performance. It can be scaled up through parallelization, and it outperforms the Cross-Entropy Method (CEM) in continuous control with learned dynamics models.
  arXiv  Detail & Related papers  (2022-10-04T07:34:06Z)
• An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search [0.0]
  We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS) We propose a dynamic sampling tree policy that efficiently allocates limited computational budget to maximize the probability of correct selection of the best action at the root node of the tree.
  arXiv  Detail & Related papers  (2022-04-26T02:39:18Z)
• A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search [41.11958980731047]
  We propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization. We show that this theoretical formulation unifies different approaches, including our newly introduced ones, under the same mathematical framework. In practice, our unified perspective offers a flexible way to balance between exploration and exploitation by tuning the single $alpha$ parameter according to the problem at hand.
  arXiv  Detail & Related papers  (2022-02-11T15:30:08Z)
• Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances [55.64521598173897]
  This paper tries to train a small-scale model, which could be repetitively used to build heat maps for the traveling salesman problem (TSP) Heat maps are fed into a reinforcement learning approach (Monte Carlo tree search) to guide the search of high-quality solutions. Experimental results show that, this new approach clearly outperforms the existing machine learning based TSP algorithms.
  arXiv  Detail & Related papers  (2020-12-19T11:06:30Z)
• Monte-Carlo Tree Search as Regularized Policy Optimization [47.541849128047865]
  We show that AlphaZero's search algorithms are an approximation to the solution of a specific regularized policy optimization problem. We propose a variant of AlphaZero which uses the exact solution to this policy optimization problem, and show experimentally that it reliably outperforms the original algorithm in multiple domains.
  arXiv  Detail & Related papers  (2020-07-24T13:01:34Z)
• On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts [5.137144629366217]
  A basic simulation-based reinforcement learning algorithm is the Monte Carlo Exploring States (MCES) method. We investigate the convergence of this algorithm for the case with undiscounted costs, also known as the shortest path problem. As a side result, we also provide a proof of a version of the supermartingale convergence theorem commonly used in approximation.
  arXiv  Detail & Related papers  (2020-07-21T16:19:09Z)
• Clustering Binary Data by Application of Combinatorial Optimization Heuristics [52.77024349608834]
  We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior optimization metaheuristics. From a set of 16 data tables generated by a quasi-Monte Carlo experiment, a comparison is performed for one of the aggregations using L1 dissimilarity, with hierarchical clustering, and a version of k-means: partitioning around medoids or PAM.
  arXiv  Detail & Related papers  (2020-01-06T23:33:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.