Questions tagged [monte-carlo-tree-search]

This tag should be used for questions about the MCTS algorithm (how/why it works, potential applications, enhancements, combinations with other algorithms, implementation, etc.)

Filter by
Sorted by
Tagged with
1
vote
0answers
43 views

How is q-learning related to game trees?

At a first look, q-learning is a revolutionary strategy in realizing Artificial Intelligence. It has to do with finding a policy, a reward structure, neural networks for storing the q-table and a ...
0
votes
0answers
18 views

Tweaking MCTS to account for opponent knowing the state of the game

I'm making an artificial intelligence for a card game using MCTS. With a standard 52-cards deck, 4 hands are dealt: 1 for each of the 3 players and one extra hand. Then, each player gets the choice to ...
4
votes
2answers
72 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-determinstic game. Apparently one of the standard approaches is to model non-determinism using chance nodes. The problem for this game is that it ...
2
votes
2answers
51 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 ...
2
votes
2answers
59 views

Monte Carlo Tree Search - Expansion w/ many children

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 non-biased-ly decide which child(...
1
vote
1answer
65 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 (...
2
votes
1answer
132 views

Monte Carlo Tree Search: start playout from leaf or child of leaf?

On Wikipedia, the MCTS algorithm is described like: 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 (...
6
votes
1answer
148 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-...
5
votes
1answer
62 views

Understanding the expansion phase in Monte Carlo Tree Search

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 ...
0
votes
0answers
34 views

Proof of Correctness of Monte Carlo Tree Search

I'm trying to make the proof of correctness of Monte Carlo Tree Search. Any help would be really appreciated.
2
votes
0answers
51 views

What is the time complexity of an unparellelized Monte Carlo tree search?

I am writing a report where I used a slightly modified version of MCTS (not parallelized). I thought It could be interesting if I could calculate its time complexity. I'd appreciate any help I could ...
3
votes
1answer
85 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 ...
4
votes
2answers
93 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 ...
0
votes
1answer
94 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 ...
2
votes
0answers
10 views

How is GARB implemented in PGRD-DL to calculate gradients w.r.t. internal rewards?

In section 3 of this paper the author outlines how GARB was adapted to reduce the variance in updating parameters to an internal reward function estimator. I have read it a number of times and ...
3
votes
1answer
19 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 “...
4
votes
2answers
71 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:/...
3
votes
0answers
43 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 ...
2
votes
1answer
76 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 ...
1
vote
1answer
51 views

Similarities and differences between UCT algorithms in (i), (ii), (iii) and (iv)?

I am trying to understand the similarities and differences between: (i) the UCT algorithm in Kocsis and Szepesvári (2006); (ii) the UCT algorithm in Section 3.3 of Browne et al (2012); (iii) the MCTS ...
1
vote
1answer
45 views

Chess Engine for low clock cycle CPU

I'm looking for a simple chess algorithm that works well on a ~132MHz CPU. I know that MiniMax would take too long to go deep in order to find good moves. Is Monte Carlo Tree Search working well on ...
2
votes
0answers
65 views

Understanding an execution of the Monte Carlo tree search algorithm

I have these Monte Carlo Tree Search, and I need to expand it, but I don't understand the step 1 and 2, why it it goes to the first Node and then make a new Node? instead of go to the depthest left ...
3
votes
2answers
225 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. ...
1
vote
2answers
254 views

Is Monte Carlo Tree Search appropriate for problems with large state and action spaces?

I'm doing a research on a finite-horizon Markov decision process with $t=1, \dots, 40$ periods. In every time step $t$, the (only) agent has to chose an action $a(t) \in A(t)$, while the agent is in ...
2
votes
1answer
107 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 ...
2
votes
1answer
156 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
votes
1answer
72 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 ...
1
vote
1answer
81 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 ...
1
vote
1answer
35 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 ...
2
votes
1answer
57 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 ...
6
votes
3answers
176 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 ...
4
votes
1answer
327 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 ...
3
votes
1answer
502 views

Monte Carlo Tree Search Expansion Phase

I'm confused regarding a specific detail of MCTS. To illustrate my question, lets take the simple example of tic-tac-toe. After the selection phase, when a leaf node is reached, the tree is expanded ...
8
votes
2answers
410 views

Does Monte Carlo Search (specifically used by AlphaZero) Qualify as Machine Learning?

To the best of my understanding, Monte Carlo Search is an alternative method to Minimax for searching a tree of nodes. It works by choosing a move (generally the one with the highest chance of being ...
12
votes
5answers
5k views

How do I choose which algorithm is best for something like a checkers board game?

I am currently new to artificial intelligence but I am very intrigued by it. I am currently researching three algorithms, namely: Minimax, Alpha-beta pruning and Monte Carlo tree search. As you may ...
6
votes
3answers
868 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 ...
-4
votes
1answer
153 views

Are recent advances in machine learning really “artificial” intelligence, or merely brute force and human design?

It sounds like people boast of something being "artificial" about machine learning when actually people boast that humans implemented algorithms like e.g. Monte Carlo Search (MCST) etc. I think the ...
4
votes
1answer
760 views

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

In MCTS, we start at root node R. Then we select some leaf node L. And we expand it by one or more child nodes and simulate from the child to end of game.image link My question is when to expand? and ...
1
vote
1answer
122 views

How to estimate the AI player's strength in multiplayer game?

I have implemented multiple MCTS based AI players for the Love Letter game (rules). It is a 2-4 players zero sum card game where players make alternating moves. I am struggling with how to properly ...
3
votes
1answer
323 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
votes
1answer
381 views

AlphaZero chess algorithm, Monte Carlo search

Recently, DeepMind's AlphaZero chess algorithm did better than the prior best chess software Stockfish. I read an arxiv paper about it but I'm not sure if: is there a value given for each piece (e.g. ...
2
votes
1answer
81 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 ...
4
votes
2answers
183 views

Determinization step in Information Set Monte Carlo Tree Search

After reading this paper about Monte Carlo methods for imperfect information games with elements of uncertainty, I couldn't understand the application of determinization step in author's ...
2
votes
1answer
369 views

Can we use MCTS/UCT without a generative model?

From what I have understood reading the UCT paper "Bandit based monte-carlo planning", MCTS/UCT requires a generative model. Does it mean, in case there is no generative model of the environment, we ...
5
votes
1answer
658 views

MCTS: Terminal (leaf) nodes in selection step

In Monte Carlo Tree Search: What does one do when the Selection step selects a node that is a Terminal state, i.e. a won/lost state (it's by definition a leaf node)? Expansion/Simulation is not in ...
1
vote
1answer
88 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 ...
10
votes
1answer
392 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 ...
5
votes
1answer
136 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 ...
15
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-...