#Algorithm

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onyx crest
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Monte Carlo Tree Search can someone explain it to me

proud veldtBOT
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<@&987246717831381062> please have a look, thanks.

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TJ-Bot
Monte Carlo Tree Search can someone explain it to me

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm used predominantly in decision processes like game playing (e.g., Go, Chess). It balances exploration and exploitation to build a search tree incrementally.

Key steps of MCTS:

  1. Selection: Starting from the root (current state), recursively select the child node with the best score (usually based on UCT - Upper Confidence Bound for Trees) until a leaf node is reached.
  2. Expansion: If the leaf is not terminal, expand it by adding one or more child nodes.
  3. Simulation (Rollout): From the expanded node, simulate a random playout (play moves randomly until the end or a defined depth).
  4. Backpropagation: Update the nodes on the path from the expanded node back to the root based on the simulation result.

Over many iterations, MCTS converges toward the optimal move by sampling the most promising moves more often.

The strength of MCTS lies in combining random sampling (Monte Carlo) to estimate outcomes with a tree search that refines these estimates over time.

Useful links:

- AI generated response using gpt-4.1-mini model
rigid tartan
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monte carlo just means "guess randomly"

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so there is some degree of "guessing randomly" in the search, probably just randomly going down some paths in the tree

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maybe weighted in some way

dire spoke
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Markov Chain text generators (pre-cursor to AI) use a randomly accessed graph/tree to generate the next likely word given a prefix of words.

Monte Carlo Tree Search could also mean a strategy to explore a solution set that is too large to brute force. Randomly poke around and find good candidate solutions to explore.