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A curated list of Monte Carlo tree search papers with implementations.

License: Creative Commons Zero v1.0 Universal

Python 100.00%
tree-search learning machine-learning deep-learning monte-carlo monte-carlo-tree-search reinforcement-learning reinforcement-learning-agent atari rl

awesome-monte-carlo-tree-search-papers's Issues

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I maintain a similar list at https://github.com/captn3m0/boardgame-research/ and find it easier to link the DOI instead (wherever possible)

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Color-coding algorithms

Hi @benedekrozemberczki, this is an interesting collection, thanks!

I work as a researcher mostly in the intersection of semantic technologies, database systems, and high-performance computing so I might be able to add some papers. But first, I am not 100% sure about what the exact definition of "tree search" is here (and I guess, there is a number of possible definitions). However, I have recently stumbled upon "color-coding algorithms" use repeated relabeling operations to count/enumerate isomorphic matches of subtrees. Some papers that could be relevant are as follows:

  • ISMB'08: Alon et al.'s "Biomolecular network motif counting and discovery by color coding", one of the seminal papers
  • TMSCS'18: color coding in MapReduce: "In this paper, we present SAHAD, a MapReduce algorithm for detecting and counting trees of bounded size using the elegant color coding technique developed by N. Alon et al. SAHAD is a randomized algorithm, and we show rigorous bounds on the approximation quality and the performance of it."
  • preprint'19: arbitrary subgraph matching using the GraphBLAS* standard which defines linear algebraic operations for graph algorithms. These states in the introduction that "Alon et al. [2, 3] provide a practical algorithm to count trees and graphs of bounded treewidth (size less than 10) from PPINs of unicellular and multicellular organisms by using the color-coding technique developed in [4]"

Let me know if this seems relevant to how the "tree search" problem is defined in the ML/DM communities (and in this repository).

*Regarding GraphBLAS, I happen to have my own awesome-style collection of resources, including papers, presentations, implementations, etc.

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