PDF] Monte-Carlo Graph Search for AlphaZero
Por um escritor misterioso
Last updated 25 março 2025
![PDF] Monte-Carlo Graph Search for AlphaZero](https://d3i71xaburhd42.cloudfront.net/4bafaf654937500f1a6a7c0df9c4f548f1c27e78/8-Figure5-1.png)
A new, improved search algorithm for AlphaZero is introduced which generalizes the search tree to a directed acyclic graph, which enables information flow across different subtrees and greatly reduces memory consumption. The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero.
![PDF] Monte-Carlo Graph Search for AlphaZero](https://media.arxiv-vanity.com/render-output/8351841/board_game_result/connect4.jpeg)
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios – arXiv Vanity
![PDF] Monte-Carlo Graph Search for AlphaZero](https://www.pnas.org/cms/10.1073/pnas.2206625119/asset/71bb680a-7bbc-4926-99a3-1550641d0b5a/assets/images/large/pnas.2206625119fig04.jpg)
Acquisition of chess knowledge in AlphaZero
![PDF] Monte-Carlo Graph Search for AlphaZero](https://www.pnas.org/cms/10.1073/pnas.2206625119/asset/1bdc1e91-9dbe-4a5f-85dd-e35b9909fd2d/assets/images/large/pnas.2206625119fig05.jpg)
Acquisition of chess knowledge in AlphaZero
![PDF] Monte-Carlo Graph Search for AlphaZero](https://www.pnas.org/cms/10.1073/pnas.2206625119/asset/15e059ed-014c-4040-93cd-f383b87c213f/assets/images/large/pnas.2206625119fig03.jpg)
Acquisition of chess knowledge in AlphaZero
![PDF] Monte-Carlo Graph Search for AlphaZero](https://miro.medium.com/v2/resize:fit:4000/1*0pn33bETjYOimWjlqDLLNw.png)
AlphaGo Zero Explained In One Diagram, by David Foster, Applied Data Science
![PDF] Monte-Carlo Graph Search for AlphaZero](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs40535-018-0052-y/MediaObjects/40535_2018_52_Fig4_HTML.png)
Deep bidirectional intelligence: AlphaZero, deep IA-search, deep IA-infer, and TPC causal learning, Applied Informatics
![PDF] Monte-Carlo Graph Search for AlphaZero](https://miro.medium.com/v2/resize:fit:802/1*G_36bdKMuMbYicziS2zwiQ.png)
Why Player Of Games Is Needed. Comparison Between Player of Games…, by Ben Bellerose
![PDF] Monte-Carlo Graph Search for AlphaZero](https://media.springernature.com/m685/springer-static/image/art%3A10.1186%2Fs40535-018-0052-y/MediaObjects/40535_2018_52_Fig1_HTML.png)
Deep bidirectional intelligence: AlphaZero, deep IA-search, deep IA-infer, and TPC causal learning, Applied Informatics
![PDF] Monte-Carlo Graph Search for AlphaZero](https://d3i71xaburhd42.cloudfront.net/4bafaf654937500f1a6a7c0df9c4f548f1c27e78/11-Table2-1.png)
PDF] Monte-Carlo Graph Search for AlphaZero
Understanding AlphaZero Neural Network's SuperHuman Chess Ability - MarkTechPost
![PDF] Monte-Carlo Graph Search for AlphaZero](https://www.science.org/cms/10.1126/sciadv.adg3256/asset/a65dfb4c-e84b-47ce-bc19-5e0abd165b17/assets/images/large/sciadv.adg3256-f4.jpg)
Student of Games: A unified learning algorithm for both perfect and imperfect information games
AlphaZero: Shedding new light on chess, shogi, and Go - Google DeepMind
![PDF] Monte-Carlo Graph Search for AlphaZero](https://ars.els-cdn.com/content/image/1-s2.0-S0952197621002700-gr3.jpg)
Learning to traverse over graphs with a Monte Carlo tree search-based self-play framework - ScienceDirect
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