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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16
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3answers
7k 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,...
16
votes
1answer
2k views

How does “Monte-Carlo search” work?

I have heard about this concept in a Reddit post about Alpha Go. I have tried to go through the paper and the article, but could not really make sense of the algorithm. So, can someone give an easy-...
10
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3answers
746 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 ...
10
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1answer
470 views

Monte Carlo Tree Search: What kind of moves can easily be found and what kinds make trouble?

I want to start with a scenario that got me thinking about how well MCTS can perform: Let's assume there is a move that is not yet added to the search tree. It is some layers/moves too deep. But if we ...
8
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2answers
845 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 ...
8
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2answers
156 views

How can alpha zero learn if the tree search stops and restarts before finishing a game?

I am trying to understand how alpha zero works, but there is one point that I have problems understanding, even after reading several different explanations. As I understand it (see for example https:/...
7
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1answer
164 views

How do I know when to use which Monte Carlo method?

I'm a bit confused with extensive number of different Monte Carlo methods such as: Hamiltonian/Hybrid Monte Carlo (HMC), Dynamic Monte Carlo (DMC), Markov chain Monte Carlo (MCMC), Kinetic Monte ...
7
votes
1answer
459 views

Does AlphaZero use Q-Learning?

I was reading the AlphaZero paper Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, and it seems they don't mention Q-Learning anywhere. So does AZ use Q-...
7
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1answer
959 views

What should we do when the selection step selects a terminal state?

In Monte Carlo tree search, what should we do when the selection step selects a terminal state (i.e. a won or lost state), which is, by definition, a leaf node? Expansion and simulation is not in ...
7
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3answers
1k views

Why does Monte Carlo work when a real opponent's behavior may not be random

I am learning about Monte Carlo algorithms and struggling to understand the following: If simulations are based on random moves, how can the modeling of the opponent's behavior work well? For ...
6
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3answers
222 views

Would AlphaGo Zero become perfect with enough training time?

Would AlphaGo Zero become theoretically perfect with enough training time? If not, what would be the limiting factor? (By perfect, I mean it always wins the game if possible, even against another ...
6
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1answer
404 views

When should Monte Carlo Tree search be chosen over MiniMax?

I would like to ask whether MCTS is usually chosen when the branching factor for the states that we have available is large and not suitable for Minimax. Also, other than MCTS simluates actions, where ...
6
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1answer
398 views

MCTS: How to choose the final action from the root

When the time allotted to Monte Carlo tree search runs out, what action should be chosen from the root? The original UCT paper (2006) says bestAction in their ...
6
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1answer
278 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 ...
5
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2answers
173 views

Why AlphaGo didn't use Deep Q-Learning?

In the previous research, in 2015, Deep Q-Learning shows its great performance on single player Atari Games. But why do AlphaGo's researchers use CNN + MCTS instead of Deep Q-Learning? is that because ...
5
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2answers
236 views

What is the appropriate way to deal with multiple paths to same state in MCTS?

Many games have multiple paths to the same states. What is the appropriate way to deal with this in MCTS? If the state appears once in the tree, but with multiple parents, then it seems to be ...
5
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1answer
1k views

When to expand and when to simulate in Monte Carlo Tree Search?

In Monte Carlo Tree Search (MCTS), we start at root node $R$. Then we select some leaf node $L$. And we expand $L$ by one or more child nodes and simulate from the child to the end of the game. When ...
5
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2answers
190 views

What is a simple game for validation of MCTS?

What is a simple turn-based game, that can be used to validate a Monte-Carlo Tree Search code and it's parameters? Before applying it to problems where I do not have a possiblity to validate its ...
5
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1answer
102 views

How does Hearthstone AI deal with random events

I want to learn a lot about the AI of CCG, such as Hearthstone. And now I have known one of the main algorithms that used in this kind of games, MCTS. It analyses the most promising moves, and expands ...
5
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1answer
941 views

Which nodes are expanded in the expansion phase of MCTS?

I'm confused regarding a specific detail of MCTS. To illustrate my question, let's take the simple example of tic-tac-toe. After the selection phase, when a leaf node is reached, the tree is expanded ...
5
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2answers
302 views

In this implementation of the Information Set Monte Carlo Tree Search, why can't the players see the cards of each other?

After reading this paper about Monte Carlo methods for imperfect information games with elements of uncertainty, I couldn't understand the application of the determinization step in the author's ...
4
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2answers
720 views

Rollout algorithm like Monte Carlo search suggest model based reinforcement learning?

From what I understand, Monte Carlo Tree Search Algorithm is a solution algorithm for model free reinforcement learning (RL). Model free RL means agent doesnt know the transition and reward model. ...
4
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1answer
591 views

Algorithms for games with very high branching factors (Connect6)

Connect6 is an example of a game with a very high branching factor. It is about 45 thousand, dwarfing even the impressive Go. What algorithms can you use on games with such high branching factors? I ...
4
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1answer
514 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, ...
4
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1answer
58 views

How does AlphaZero's MCTS work when starting from the root node?

From the AlphaGo Zero paper, during MCTS, statistics for each new node are initialized as such: ${N(s_L, a) = 0, W (s_L, a) = 0, Q(s_L, a) = 0, P (s_L, a) = p_a}$. The PUCT algorithm for selecting ...
4
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1answer
497 views

Is there any value given to each chess piece in AlphaZero? [closed]

Recently, DeepMind's AlphaZero chess algorithm did better than the prior best chess software Stockfish. I read the paper Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning ...
4
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1answer
78 views

How could an AI detect whether an enemy in a game can be blocked off/trapped?

Imagine a game played on a 10x10 grid system where a player can move up down left or right and imagine there are two players on this grid: An enemy and you. In this game, there are walls on the grid ...
3
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1answer
162 views

How Does AlphaGo Zero Implement Reinforcement Learning?

AlphaGo Zero (https://deepmind.com/blog/alphago-zero-learning-scratch/) has several key components that contribute to it's success: A Monte Carlo Tree Search Algorithm that allows it to better search ...
3
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1answer
674 views

How fast does Monte Carlo tree search converge?

How fast does Monte Carlo Tree Search converge? Is there proof that it does converge? How does it compare to Temporal Difference learning in terms of convergence speed (assuming the evaluation step ...
3
votes
1answer
92 views

Do AIs based on MCTS start each game from scratch?

AIs that rely on MCTS - like AlphaGo - create their decision tree as the game progresses. Do they start from scratch each game and build a new tree or do they keep the tree and grow it from game to ...
3
votes
2answers
143 views

How can we efficiently and unbiasedly decide which children to generate in the expansion phase of MCTS?

When executing MCTS' expansion phase, where you create a number of child nodes, select one of the numbers, and simulate from that child, how can you efficiently and unbiasedly decide which child(ren) ...
3
votes
1answer
316 views

Several questions related to UCT and MCTS

In Bandit Based Monte-Carlo Planning, the article where UCT is introduced as a planning algorithm, there is an algorithm description in page 285 (4 of the pdf). Comparing this implementation of UCT (a ...
3
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1answer
97 views

How does Monte Carlo Tree Search UCT exploitation value change based on perspective?

In this blog toward the end, the author writes the following: For the sake of my question, let’s assume that a terminal state gives a reward of +1 for a win and -1 for a loss. When the author says “...
3
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1answer
115 views

Weighted move rating for AI

My AI (for the card game schnapsen) currently calculates every possible way the game could end and then evaluates the percentage of winning for every playable card / move. The calculation is done ...
3
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1answer
70 views

Should I use Monte Carlo or a classifier for this Decision Making problem?

I want to build a model to support decision making for loan insurance proposal. There are three actors in the problem: a bank, a loaner applicant (someone who ask for a loan) and a counselor. The ...
3
votes
1answer
412 views

Can we use MCTS without a generative model?

From what I have understood reading the UCT paper Bandit based monte-carlo planning, by Levente Kocsis and Csaba Szepesvári, MCTS/UCT requires a generative model. Does it mean that, in case there is ...
3
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1answer
197 views

How to run a Monte Carlo Tree Search MCTS for stochastic environment?

For MCTS there is an expansion phase where we make a move and list down all the next states. But this is complicated by the fact that for some games, after making the move, there is a stochastic ...
3
votes
1answer
194 views

Is the playout started from a leaf or child of leaf in Monte Carlo Tree Search?

On Wikipedia, the MCTS algorithm is described Selection: start from root $R$ and select successive child nodes until a leaf node $L$ is reached. A leaf is any node from which no simulation (playout) ...
3
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1answer
102 views

Why is Monte Carlo used as the tree search algorithm for AlphaGo?

Could a better algorithm other than Monte Carlo be used for the AlphaGo computer? Why didn't the DeepMind team think of choosing another kind of algorithm rather than spending time on their neural ...
3
votes
1answer
260 views

Why do neural nets and machine learning tend to work well with MCTS, but not with regular Minimax game-playing AI?

I've often heard MCTS grouped together with neural nets and machine learning. From what I gather, MCTS uses a refined intuition (from maching learning) to evaluate positions. This allows it to better ...
3
votes
2answers
302 views

How can I design a reinforcement learning model for a game with multiple complex actions taken at a time?

I have a steady hex-map and turn-based wargame featuring WWII carrier battles. On a given turn, a player may choose to perform a large number of actions. Actions can be of many different types, and ...
3
votes
1answer
438 views

Which Reinforcement Learning algorithms are efficient for episodic problems?

I have some episodic datasets extracted from a turn-based RTS game in which the current actions leading to the next state doesn’t determine the final solution/outcome of the episode. The learning is ...
3
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0answers
41 views

Where does reinforcement learning actually show up in Deepmind's game engines?

From the brief research I've done on the topic, it appears that the way Deepmind's Alphazero or Muzero makes decisions is through Monte Carlo tree searches, where in the randomized simulations allows ...
3
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0answers
45 views

Is Monte Carlo tree search needed in partially observable environments during gameplay?

I understand that with a fully observable environment (chess / go etc) you can run an MCTS with an optimal policy network for future planning purposes. This will allow you to pick actions for gameplay,...
3
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0answers
49 views

How exactly does self-play work, and how does it relate to MCTS?

I am working towards using RL to create an AI for a two-player, hidden-information, a turn-based board game. I have just finished David Silver's RL course and Denny Britz's coding exercises, and so am ...
3
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0answers
149 views

How is the rollout from the MCTS implemented in both of the AlphaGo Zero and the AlphaZero algorithms?

In a vanilla Monte Carlo tree search (MCTS) implementation, the rollout is usually implemented following a uniform random policy, that is, it takes random actions until the game is finished and only ...
3
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0answers
77 views

Feature Selection using Monte Carlo Tree Search

I'm trying to tackle the problem of feature selection as an RL problem, inspired by the paper Feature Selection as a One-Player Game. I know Monte-Carlo tree search (MCTS) is hardly RL. So, I used ...
3
votes
0answers
90 views

Understanding an execution of the Monte Carlo tree search algorithm

I have the execution of the Monte Carlo Tree Search (MCTS) below. I need to expand it, but I don't understand steps 1 and 2. Why does it go to the first node and then make a new node, instead of ...
2
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1answer
58 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 ...
2
votes
1answer
40 views

Is it meaningful to give more weight to the result of monte carlo search with less turn win?

I'm programming on Connect6 with MCTS. Monte Carlo Tree Search is based on random moves. It counts up the number of wins in certain moves. (Whether it wins in 3 turns or 30 turns) Is the move with ...