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Methods › Reinforcement Learning › Heuristic Search Algorithms › Monte-Carlo Tree Search

Monte-Carlo Tree Search

introduced 2006 166 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Monte-Carlo Tree Search is a planning algorithm that accumulates value estimates obtained from Monte Carlo simulations in order to successively direct simulations towards more highly-rewarded trajectories. We execute MCTS after encountering each new state to select an agent's action for that state: it is executed again to select the action for the next state. Each execution is an iterative process that simulates many trajectories starting from the current state to the terminal state. The core idea is to successively focus multiple simulations starting at the current state by extending the initial portions of trajectories that have received high evaluations from earlier simulations.

Source: Sutton and Barto, Reinforcement Learning (2nd Edition)

Image Credit: Chaslot et al

Papers archive 2025-07-28

30 shown of 166, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 111 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Reinforcement Learning (RL)47
reinforcement-learning45
Reinforcement Learning44
Model-based Reinforcement Learning21
Decision Making20
Board Games16
Deep Reinforcement Learning14
Atari Games12
Continuous Control6
Game of Go6
continuous-control5
model5
Sequential Decision Making4
Atari Games 100k3
BIG-bench Machine Learning3
Bayesian Optimization3
Computational Efficiency3
General Reinforcement Learning3
Heuristic Search3
Machine Translation3

Usage over time archive 2025-07-28

Papers per year tagged with Monte-Carlo Tree Search: 2009 to 2025, peak 29 29 0 2009: 1 paper 2009 2010: 2 papers 2011: 0 papers 2011 2012: 0 papers 2013: 2 papers 2013 2014: 1 paper 2015: 1 paper 2015 2016: 0 papers 2017: 2 papers 2017 2018: 8 papers 2019: 13 papers 2019 2020: 29 papers 2021: 24 papers 2021 2022: 21 papers 2023: 28 papers 2023 2024: 23 papers 2025: 11 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (166 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Heuristic Search Algorithms

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