https://github.com/masouduut94/MCTS-agent-cythonized

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.
https://github.com/masouduut94/MCTS-agent-cythonized

cython last-good-reply markov-decision-processes monte-carlo-simulation monte-carlo-tree-search performance-optimization poolrave q-learning rapid-action-value-estimation reinforcement-learning tkinter

Last synced: 3 days ago
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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.

• Host: GitHub
• URL: https://github.com/masouduut94/MCTS-agent-cythonized
• Owner: masouduut94
• License: mit
• Created: 2019-11-11T15:48:25.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-03-21T05:38:34.000Z (almost 4 years ago)
• Last Synced: 2023-03-28T20:42:51.916Z (almost 2 years ago)
• Topics: cython, last-good-reply, markov-decision-processes, monte-carlo-simulation, monte-carlo-tree-search, performance-optimization, poolrave, q-learning, rapid-action-value-estimation, reinforcement-learning, tkinter
• Language: Python
• Size: 230 KB
• Stars: 11
• Watchers: 2
• Forks: 1
• Open Issues: 0

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