mlsanityData to 14 Oct 2026

Constructing Ancestral Recombination Graphs through Reinforcement Learning

arXiv 2406.12022 ↗First posted 17 Jun 2024cs.LG cs.AI stat.MLcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Over the years, many approaches have been proposed to build ancestral recombination graphs (ARGs), graphs used to represent the genetic relationship between individuals. Among these methods, many rely on the assumption that the most likely graph is among the shortest ones. In this paper, we propose a new approach to build short ARGs: Reinforcement Learning (RL). We exploit the similarities between finding the shortest path between a set of genetic sequences and their most recent common ancestor and finding the shortest path between the entrance and exit of a maze, a classic RL problem. In the maze problem, the learner, called the agent, must learn the directions to take in order to escape as quickly as possible, whereas in our problem, the agent must learn the actions to take between coalescence, mutation, and recombination in order to reach the most recent common ancestor as quickly as possible. Our results show that RL can be used to build ARGs as short as those built with a heuristic algorithm optimized to build short ARGs, and sometimes even shorter. Moreover, our method allows to build a distribution of short ARGs for a given sample, and can also generalize learning to new samples not used during the learning process.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 71 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

9 of 71 references.

References · 9

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  2. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  3. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 174
  4. 2017Applications of Deep Learning and Reinforcement Learning to Biological Datacited by 45
  5. 2018A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networkscited by 31
  6. 2019Growing Action Spacescited by 29
  7. 2021A Boosting Approach to Reinforcement Learningcited by 12
  8. 2017Teaching a Machine to Read Maps with Deep Reinforcement Learningcited by 9
  9. 2024A Survey Analyzing Generalization in Deep Reinforcement Learningcited by 8

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  1. 2024Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
  2. 2026The Time Value of Evolution
  3. 2026Genetic Algorithms with Optimization Guided Operators
  4. 2026GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
  5. 2024Reinforcement Learning for Causal Discovery without Acyclicity Constraints
  6. 2026GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization
  7. 2026From Optimal Policies to Individual Differences: Rethinking Reinforcement Learning for Biology
  8. 2026Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving