masouduut94 / MCTS-agent-python

Monte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games and planning problems. In this project I used a board game called "HEX" as a platform to test different simulation strategies in MCTS field.

Date Created 2019-10-10 (5 years ago)

Commits 29 (last one about a year ago)

Stargazers 68 (0 this week)

Watchers 2 (0 this week)

Forks 10

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RepositoryStats indexes 630,459 repositories, of these masouduut94/MCTS-agent-python is ranked #398,035 (37th percentile) for total stargazers, and #480,560 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #76,815/128,655.

masouduut94/MCTS-agent-python is also tagged with popular topics, for these it's ranked: reinforcement-learning (#963/1445)

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mcts game-of-hex decision-space sequential-decisions reinforcement-learning monte-carlo-tree-search markov-decision-processes

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updated: 2025-03-07 @ 01:58pm, id: 214206049 / R_kgDODMSGYQ