몬테카를로 트리 순회

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  • Exploration can be useful to ensure that MCTS is not overlooking any potentially better paths.[1]
  • In this process, the MCTS algorithm traverses the current tree from the root node using a specific strategy.[1]
  • MCTS uses the Upper Confidence Bound (UCB) formula applied to trees as the strategy in the selection process to traverse the tree.[1]
  • During traversal, once a child node is found which is also a leaf node, the MCTS jumps into the expansion step.[1]
  • Monte Carlo Tree search is a fancy name for one Artificial Intelligence algorithm used specially in games.[2]
  • MCTS, like the name says, is a way of searching a tree.[2]
  • Monte Carlo Tree Search is an algorithm used when playing a so-called perfect information game.[3]
  • In its simplest and most memory efficient implementation, MCTS will add one child node per iteration.[4]
  • This is the algorithm used in the vast majority of current MCTS implementations.[4]
  • Benefits MCTS offers a number of advantages over traditional tree search methods.[4]
  • MCTS performs asymmetric tree growth that adapts to the topology of the search space.[4]
  • As Professor Williams just said, we are going to be talking about Monte Carlo tree search today.[5]
  • By the end of this presentation, you will know not only why we care about Monte Carlo tree searches.[5]
  • And second, we'll be going through the pros and cons of MCTS, as well as the algorithm itself.[5]
  • And so that's why today we're going to be talking about Monte Carlo tree searches.[5]
  • Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces.[6]
  • While MCTS is believed to provide an approximate value function for a given state with enough simulations, cf.[7]
  • In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning.[8]
  • MCTS is based on randomized explorations of the search space.[9]
  • This chapter gives an overview of both classical and MCTS approaches to computer Go.[10]
  • This paper explores the possibility of applying Monte Carlo Tree Search (MCTS) technique to general purpose program synthesis.[11]
  • Figure 1 shows the high-level overview of how MCTS grows the search tree.[11]
  • The Programming Game: Evaluating MCTS as an Alternative to GP for Symbolic Regression.[11]
  • This study employs the MCTS as the search algorithm, which describes a molecule by a graph structure.[12]
  • To generate molecules with one or more branches, we rejected the no-branch molecules during the rollout operation of MCTS.[12]
  • The MCTS proposes the next molecule encoded in SMILES, and then the fast evaluation by MD simulations provides its VI ASTM as feedback.[12]
  • D 2270 standard (VI ASTM ) updates the MCTS policy to improve the next set of candidate molecules.[12]
  • Comparing the obtained win-loss ratios, we examine behavior of Monte Carlo tree search (MCTS) in Knight-Amazons.[13]
  • Then we execute an upper confidence bounds applied to trees (UCT) program as MCTS and find which moves the UCT program chooses most often.[13]
  • Monte Carlo tree search applies Monte Carlo method to the game tree search.[14]
  • We'll design a generalized solution for MCTS which can be utilized for many other board games as well.[14]
  • If MCTS is used in its basic form without any improvements, it may fail to suggest reasonable moves.[14]
  • However, MCTS can be improved using some techniques.[14]
  • In this article, I will introduce you to the algorithm at the heart of AlphaGo – Monte Carlo Tree Search (MCTS).[15]
  • In this blog, we will focus on the working of Monte Carlo Tree Search only.[15]
  • The way MCTS works is that we run it for a defined number of iterations or until we are out of time.[15]
  • MCTS plays the primary role in making complex games like Go easier to crack in a finite amount of time.[15]
  • 4 — MCTS can return a recommended move at any time because the statistics about the simulated games are constantly updated.[16]
  • The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space.[17]
  • The application of Monte Carlo tree search in games is based on many playouts, also called roll-outs.[17]
  • In particular, pure Monte Carlo tree search does not need an explicit evaluation function.[17]
  • The game tree in Monte Carlo tree search grows asymmetrically as the method concentrates on the more promising subtrees.[17]
  • MCTS has also been applied in materials science and engineering.[18]
  • The section “Monte Carlo tree search” presents the MCTS algorithm.[18]
  • Within a computational budget, MCTS explores the search space over multiple iterations.[18]
  • An open source implementation of MCTS was developed by Dieb et al.[18]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Monte Carlo Tree Search (MCTS) - GeeksforGeeks
  2. ↑ 2.0 2.1 AI: Monte Carlo Tree Search (MCTS)
  3. ↑ Monte Carlo Tree Search: An Introduction - Appsilon
  4. ↑ 4.0 4.1 4.2 4.3 Monte Carlo Tree Search
  5. ↑ 5.0 5.1 5.2 5.3 Advanced Lecture 4: Monte Carlo Tree Search
  6. ↑ Learning decision trees through Monte Carlo tree search: An empirical evaluation
  7. ↑ Non-Asymptotic Analysis of Monte Carlo Tree Search
  8. ↑ PROGRESSIVE STRATEGIES FOR MONTE-CARLO TREE SEARCH
  9. ↑ Monte-Carlo Tree Search
  10. ↑ Monte-Carlo Tree Search and Computer Go
  11. ↑ 11.0 11.1 11.2 Field Report: Applying Monte Carlo Tree Search for Program Synthesis
  12. ↑ 12.0 12.1 12.2 12.3 Autonomous molecular design by Monte-Carlo tree search and rapid evaluations using molecular dynamics simulations
  13. ↑ 13.0 13.1 Analysis of a Monte Carlo Tree Search in Knight-Amazons ☆
  14. ↑ 14.0 14.1 14.2 14.3 Monte Carlo Tree Search for Tic-Tac-Toe Game
  15. ↑ 15.0 15.1 15.2 15.3 Monte Carlo Tree Search Tutorial
  16. ↑ Monte Carlo Tree Search: Implementing Reinforcement Learning in Real-Time Game Player
  17. ↑ 17.0 17.1 17.2 17.3 Monte Carlo tree search
  18. ↑ 18.0 18.1 18.2 18.3 Monte Carlo tree search for materials design and discovery

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  • [{'LOWER': 'monte'}, {'LOWER': 'carlo'}, {'LOWER': 'tree'}, {'LEMMA': 'search'}]
  • [{'LOWER': 'monte'}, {'OP': '*'}, {'LOWER': 'carlo'}, {'LOWER': 'tree'}, {'LEMMA': 'search'}]
  • [{'LEMMA': 'MCTS'}]
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