The Tree Reconstruction Game: Phylogenetic Reconstruction Using Reinforcement Learning

Dana Azouri, Oz Granit, Michael Alburquerque, Yishay Mansour, Tal Pupko, Itay Mayrose

Research output: Contribution to journalArticlepeer-review


The computational search for the maximum-likelihood phylogenetic tree is an NP-hard problem. As such, current tree search algorithms might result in a tree that is the local optima, not the global one. Here, we introduce a paradigm shift for predicting the maximum-likelihood tree, by approximating long-term gains of likelihood rather than maximizing likelihood gain at each step of the search. Our proposed approach harnesses the power of reinforcement learning to learn an optimal search strategy, aiming at the global optimum of the search space. We show that when analyzing empirical data containing dozens of sequences, the log-likelihood improvement from the starting tree obtained by the reinforcement learning-based agent was 0.969 or higher compared to that achieved by current state-of-the-art techniques. Notably, this performance is attained without the need to perform costly likelihood optimizations apart from the training process, thus potentially allowing for an exponential increase in runtime. We exemplify this for data sets containing 15 sequences of length 18,000 bp and demonstrate that the reinforcement learning-based method is roughly three times faster than the state-of-the-art software. This study illustrates the potential of reinforcement learning in addressing the challenges of phylogenetic tree reconstruction.

Original languageEnglish
Article numbermsae105
JournalMolecular Biology and Evolution
Issue number6
StatePublished - 1 Jun 2024


  • artificial intelligence
  • evolution
  • machine learning
  • molecular biology
  • phylogenetics
  • reinforcement learning

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics


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