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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6
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2answers
313 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 ...
0
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1answer
62 views

Why aren’t heuristics for Connect Four Monte Carlo tree search improving the agent?

I’ve created an agent using MCTS to play Connect Four. It wins against humans pretty well, but I’d like to improve upon it. I decided to add domain knowledge to the MCTS rollout stage. My evaluation ...
2
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1answer
40 views

How UCT in MCTS selection phase avoids starvation?

The first step of MCTS is to keep choosing nodes based on Upper Confidence Bound applied to trees (UCT) until it reaches a leaf node where UCT is defined as $$\frac{w_i}{n_i}+c\sqrt{\frac{ln(t)}{n_i}},...
0
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1answer
30 views

Does Monte Carlo Tree Search not work on games without the same initial state?

I'm curious how you would apply Monte Carlo Tree Search to a game that has a random initial state. You generate a tree where the root node is the initial state, then you expand if the options from ...
2
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0answers
47 views

Is this a good approach to evaluate the game state with a neural network?

I've written a Monte Carlo Tree Search player for the game of Castle (AKA Shithead, Shed, Palace...). I have set this MCTS player to play against a basic rule-based AI for ~30000 games and collected ~...
2
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1answer
701 views

Game AI evaluation function and making progress towards winning

In two-player games, the exact value of the evaluation function doesn't matter, as long as it's bigger for better positions. However, for learning, it's customary when it does change when the best ...
0
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0answers
24 views

How does the second phase of the evaluation function described in this article work?

I am trying to create an evaluation function for a general game player based on the research from this article An Automatically-Generated Evaluation Function in General Game Playing. I can't ...
3
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1answer
25 views

What should the initial UCT value be with MCTS, when leaf's simulation count is zero? Infinity?

I am implenting a Monte Carlo Tree Search algorithm, where the selection process is done through Upper Confidence Bound formula: ...
0
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0answers
40 views

Alpha Zero does not converge for Connect 6, a game with huge branching factor - why?

I have a problem with applying alpha zero self-play to a game (Connect 6) with a huge branching factor (30,000 on average). I have implemented the MCTS as described but I found that during the MCTS ...
2
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1answer
107 views

How can I reduce combinatorial explosion in an MCTS-like algorithm for program induction?

I'd like to develop an MCTS-like (Monte Carlo Tree Search) algorithm for program induction, i.e. learning programs from examples. My initial plan is for nodes to represent programs and for the search ...
3
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2answers
313 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 ...
0
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0answers
28 views

MCTS: What if all children of a node are terminal?

If all children Nodes of the selected node are terminal in the selection phase - you obviously run into a problem. So how do I prevent a Node to be selected that only has terminal children?
3
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1answer
66 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 ...
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0answers
29 views

Are inputs into AlphaZero the same during the evaluate step in MCTS and during test time?

From the AlphaZero paper: The input to the neural network is an N × N × (M T + L) image stack that represents state using a concatenation of T sets of M planes of size N × N . Each set of planes ...
1
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1answer
26 views

Unclear definition of a “leaf” and diverging UTC values in the Monte Carlo Tree Search

I have two questions regarding the Selection and Expansion steps in the Monte Carlo Tree Search Algorithm. In order to state the questions, I recall the algorithm that I believe is the one most ...
1
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1answer
63 views

MCTS moves with multiple parents

I'd like to develop an MCTS-like (Monte Carlo Tree Search) algorithm for program induction, i.e. learning programs from examples. My initial plan is for nodes to represent programs and for the search ...
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 ...
1
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0answers
34 views

Why is the ideal exploration parameter in the UCT algorithm $\sqrt{2}$?

From Wikipedia, in the Monte-Carlo Tree Search algorithm, you should choose the node that maximizes the value: $${\displaystyle {\frac {w_{i}}{n_{i}}}+c{\sqrt {\frac {\ln N_{i}}{n_{i}}}}},$$ where ${...
0
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0answers
75 views

What would be the AlphaGo's performance in continuous action space?

During my research for Google DeepMind's Go-playing program Alpha Go and its successor Alpha Go Zero, I discovered that the system uses a clever pipeline and an interplay of blocks of both policy and ...
17
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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,...
3
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1answer
69 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
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1answer
37 views

How to choose the first action in a Monte Carlo Tree Search?

I'm working on reimplementing the MuZero paper. In the description of the MCTS (page 12), they indicate that a new node with associated state $s$ is to be initialized with $Q(s,a) = 0$, $N(s,a) = 0$ ...
0
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1answer
108 views

How does the MCTS tree look like?

I have come across the Monte Carlo tree search (MCTS) algorithm, but I can't find what the tree should look like. For example, does it still represent a minimax process, i.e. player 1 from the root ...
2
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1answer
65 views

Why is tree search/planning used in reinforcement learning?

In AlphaGo Zero, MCTS is used along with policy networks. Some sources say MCTS (or planning in general) increases the sample efficiency. Assumed the transition model is known and the computational ...
1
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2answers
674 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 ...
1
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1answer
78 views

Chess Neural Network - Most Optimal Input vector/matrix?

I'm wanting to build a NN that can create a policy for each possible state. I want to combine this with MCTS to eliminate randomness so when expansion occurs, I can get the probability of the move to ...
1
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1answer
58 views

AI to play a solo card game

I would like to create an AI for the 1 player version of the card game called "The Game" by Steffen Benndorf (rules here: https://nsv.de/wp-content/uploads/2018/05/the-game-english.pdf). The ...
1
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0answers
36 views

Is Monte Carlo tree search guaranteed to converge to the optimal solution in two player zero-sum stochastic games?

I'm aware that convergence proofs for Monte Carlo tree search exist in the case of deterministic zero sum games and Markov decision processes. I have come across research which applies MCTS to zero-...
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0answers
29 views

How good should I be expecting my Monte Carlo Tree Search to be?

I've been learning how the Monte Carlo Tree Search works and implemented it to play a game of Connect 4. I think I've got the algorithm correct as it plays the game quite well (it can usually beat me ...
4
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1answer
512 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 ...
0
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1answer
73 views

What is the search depth of AlphaGo and AlphaGo Zero?

I cannot find reliable sources but someone says it is 40 moves and someone else says it is 50+ moves. I read their papers and they use value function (NN) and policy function to trim the tree, so more ...
1
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1answer
75 views

In Alpha(Go)Zero, why is the policy extracted from MCTS better than the network one?

I've read through the Alpha(Go)Zero paper and there is only one thing I don't understand. The paper on page 1 states: The MCTS search outputs probabilities π of playing each move. These search ...
1
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1answer
81 views

How AlphaGo Zero is learning from $\pi_t$ when $z_t = -1$?

I have questions on the way AlphaGo Zero is trained. From original AlphaGo Zero paper, I knew that AlphaGo Zero agent learns a policy, value functions by the gathered data $\{(s_t, \pi_t, z_t)\}$ ...
3
votes
1answer
261 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 ...
0
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0answers
68 views

Monte Carlo Tree search UCB1 for Tic-Tac-Toe help

I am trying to code a MCTS agent for tic tac toe and i have some theoretical questions regarding MCTS. 1)I am using the UCB1 MCTS $UCB(Si)=average value + 2*sqrt(ln(N)/ni)$ . Considering the image ...
6
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1answer
535 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 ...
5
votes
2answers
179 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 ...
0
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0answers
48 views

How to set up MCTS and draw by repetition?

I am running a MCTS as part of a game ML algorithm. The game that it is playing can reach the same state via multiple paths. It can also reach states again. This is similar to how a position on a ...
10
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3answers
787 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 ...
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 ...
0
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0answers
31 views

How to observe or measure convergence of Monte Carlo Tree Search?

As above: how does one observe/measure a Monte Carlo Tree Search to be able to update the algorithm and compare results?
2
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1answer
76 views

Should Monte Carlo tree search be able to consistently beat me in the connect four game?

I've implemented the Monte Carlo tree search (MCTS) algorithm for a connect four game I've built. The MCTS agent beats a random choice agent 90-100% of the time, but I’m still able to beat it pretty ...
2
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1answer
125 views

How to apply hyperparameter optimization on Monte Carlo Tree Search?

I have a basic MCTS agent for the game of Hex (a turn based game). I want to tune the parameters of UCT (the Cp parameter) and the number of rollouts parameter. Where do I have to begin? The problem ...
0
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0answers
32 views

Why would you ignore episodes that loop back on the starting state in MCTS?

After reading about MCTS for policy learning and optimization, I don't understand why you would want to ignore episodes that loop back on the starting state. What advantage does this have and why ...
3
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0answers
50 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,...
2
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0answers
98 views

MCTS RAVE performing badly in Board Game AI

I'm using Monte Carlo Tree Search with UCT selection to try and build an AI player for a complex multiplayer board game. My regular UCT MCTS seems to be working fine, winning with random and basic ...
3
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0answers
50 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 ...
6
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1answer
456 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 ...
1
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0answers
44 views

Formulating MCTS with random outcomes of actions?

I am working on implementing MCTS for a scheduling problem where MCTS is formulated each time there are multiple jobs that need to be scheduled. When a job is executed, the resulting state of the ...
1
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2answers
265 views

How to understand the 4 steps of Monte Carlo Tree Search

From many blogs and this one https://web.archive.org/web/20160308070346/http://mcts.ai/about/index.html We know that the process of MCTS algorithm has 4 steps. Selection: Starting at root node R,...