Questions tagged [monte-carlo-tree-search]

For questions related to Monte Carlo Tree Search (MCTS), which is a best-first, rollout-based tree search algorithm. MCTS gradually improves its evaluations of nodes in the trees using (semi-)random rollouts through those nodes, focusing a larger proportion of rollouts on the parts of the tree that are the most promising.

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17
votes
3answers
8k views

How do I choose the best algorithm for a board game like checkers?

How do I choose the best algorithm for a board game like checkers? So far, I have considered only three algorithms, namely, minimax, alpha-beta pruning, and Monte Carlo tree search (MCTS). Apparently,...
8
votes
2answers
1k views

MCTS for non-deterministic games with very high branching factor for chance nodes

I'm trying to use a Monte Carlo Tree Search for a non-deterministic game. Apparently, one of the standard approaches is to model non-determinism using chance nodes. The problem for this game is that ...
10
votes
3answers
845 views

Does Monte Carlo tree search qualify as machine learning?

To the best of my understanding, the Monte Carlo tree search (MCTS) algorithm is an alternative to minimax for searching a tree of nodes. It works by choosing a move (generally, the one with the ...
6
votes
1answer
314 views

When does the selection phase exactly end in MCTS?

All sources I can find provide a similar explanation to each phase. In the Selection Phase, we start at the root and choose child nodes until reaching a leaf. Once the leaf is reached (assuming the ...
4
votes
1answer
638 views

Any interesting ways to combine Monte Carlo tree search with the minimax algorithm?

I've been working on a game-playing engine for about half a year now, and it uses the well known algorithms. These include minimax with alpha-beta pruning, iterative deepening, transposition tables, ...
3
votes
1answer
71 views

AlphaGo Zero: does $Q(s_t, a)$ dominate $U(s_t, a)$ in difficult game states?

AlphaGo Zero AlphaGo Zero uses a Monte-Carlo Tree Search where the selection phase is governed by $\operatorname*{argmax}\limits_a\left( Q(s_t, a) + U(s_t, a) \right)$, where: the exploitation ...
1
vote
1answer
119 views

How do you generate the transition probabilities of a non-trivial MDP?

I understand an MDP (Markov Decision Process) model is a tuple of $\{S, A, P, R \}$ where: $S$ is a discrete set of states $A$ is a discrete set of actions $P$ is the transition matrix ie. $P(s' \mid ...