# DeepMind: The Hanabi Card Game Is the Next Frontier for AI Research

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=cD-eXjf854Q
- **Дата:** 19.03.2019
- **Длительность:** 3:54
- **Просмотры:** 61,323
- **Источник:** https://ekstraktznaniy.ru/video/14341

## Описание

📝 The paper "The Hanabi Challenge: A New Frontier for AI Research" and a blog post is available here:
https://arxiv.org/abs/1902.00506
http://www.marcgbellemare.info/blog/a-cooperative-benchmark-announcing-the-hanabi-learning-environment/

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## Транскрипт

### Intro []

dear fellow Scholars this is two minute papers with K here now get this after defeating chess go and making incredible progress in Starcraft 2 scientists at Deep Mind just published a paper where they claim that Hanabi is the next Frontier in AI research and we shall stop right here I hear you asking me

### What is Hanabi [0:22]

caroy after defeating all of these immensely difficult games now you're trying to tell me that somehow this silly card game is the next step yes that's exactly what I'm saying let me explain Hanabi is a card game where two to five players cooperate to build Five Card sequences and to do that they are only allowed to exchange very little information this is also an imperfect information game which means the players don't have all the knowledge available needed to make a good decision they have to work with what they have and try to infer the rest for instance poker is also an imperfect information game because we don't see the cards of the other players and the game revolves around our guesses as to what they might have in Hanabi interestingly it is the

### Poker [1:10]

other way around so we see the cards of the other players but not our own ones the players have to work around this limitation by relying on each other and working out communication protocols and infer intent in order to win the game like in many of the best games these Simple Rules conceal a vast array of strategies all of which are extremely hard to teach to current learning

### Game Settings [1:37]

algorithms in the paper a free and open- Source system is proposed to facilitate further research works and assess the performance of currently existing techniques the difficulty level of this game can also be made easier or harder at will from both inside and outside the game and by inside I mean that we can set parameters like the number of allowed mistakes that can be made before the game is considered lost the outside part means that two main game settings are proposed one selfplay this is the easier case where the AI plays with copies of itself therefore it knows

### Conclusion [2:15]

quite a bit about its teammates and two ad hoc teams can also be constructed which means that a set of Agents need to cooperate that are not familiar with each other this is immensely difficult when I looked at the paper I expected that as we have many powerful learning algorithms they would rip through this challenge with ease but surprisingly I found out that even the easier selfplay variant severely underperforms compared to the best human players and handcrafted Bots there's plenty of work to be done here and luckily you can also run it yourself at home and train some of these agents on a consumer graphics card note that it is possible to create a handcrafted program that plays this game well as we humans already know good strategies however this project is about getting several instances of an AI to learn new ways to communicate with each other effectively again the goal is not to get a computer program that plays Hanabi well the goal is to get an AI to learn to communicate effectively and work together towards a common goal much like chess Starcraft 2 and DOTA Hanabi is still a proxy to be used for measuring progress in AI research nobody wants to spend millions of dollars to play card games at work so the final goal of Deep Mind is to reuse this algorithm for other applications where even we humans falter I have included some more materials on this game in the video description make sure to have a look thanks for watching and for your generous support and I'll see you next time