https://www.youtube.com/watch?v=0slFo1rV0EM
️ Become The AI Epiphany Patreon ️ https://www.patreon.com/theaiepiphany In this video I cover AlphaGo Zero (and AlphaZero), an ag

https://deepmind.google/discover/blog/alphazero-shedding-new-light-on-chess-shogi-and-go/
In Go, AlphaZero defeated AlphaGo Zero, winning 61% of games. However, it was the style in which AlphaZero plays these games that players may find most fascinating. In Chess, for example, AlphaZero independently discovered and played common human motifs during its self-play training such as openings, king safety and pawn structure.

https://www.classcentral.com/course/youtube-deepmind-s-alphago-zero-and-alphazero-rl-paper-explained-127004
This course covers DeepMind's AlphaGo Zero and AlphaZero, explaining how these agents learned to beat human players and algorithms in Go, Chess, and Shogi through self-play and zero human knowledge. The learning outcomes include understanding AlphaGo Zero and AlphaZero, their inner workings, training details, results, and the impact of

https://deepmind.google/discover/blog/alphago-zero-starting-from-scratch/
The paper introduces AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history. Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn

https://www.wired.com/story/deepmind-alphago-zero-nature-reinforcement-learning/
In its internal testing, detailed in the Nature paper, the firm says Zero was able to beat all of its previous versions: AlphaGo Master, AlphaGo Lee, AlphaGo Fan, Crazy Stone, Pachi and GruGo

https://www.nature.com/articles/nature24270
Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games.

https://paperswithcode.com/method/alphazero
AlphaZero. Introduced by Silver et al. in Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Edit. AlphaZero is a reinforcement learning agent for playing board games such as Go, chess, and shogi. Source: Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

https://www.smithsonianmag.com/innovation/google-ai-deepminds-alphazero-games-chess-and-go-180970981/
December 10, 2018. Google's new artificial intelligence program, AlphaZero, taught itself to play chess, shogi, and Go in a matter of hours, and outperforms the top-ranking AIs in the gameplay

https://en.wikipedia.org/wiki/AlphaZero
AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go.This algorithm uses an approach similar to AlphaGo Zero.. On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which within 24 hours of training achieved a superhuman level of play in these three games by defeating world

https://en.wikipedia.org/wiki/AlphaGo_Zero
AlphaGo Zero is a version of DeepMind's Go software AlphaGo.AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the

https://www.youtube.com/watch?v=UzYeqAJ2bA8
DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better

https://www.newyorker.com/science/elements/how-the-artificial-intelligence-program-alphazero-mastered-its-games
The result, AlphaGo Zero, detailed in a paper published in October, 2017, was so called because it had zero knowledge of Go beyond the rules. This new program was much less well-known; perhaps you

https://arstechnica.com/science/2018/12/move-over-alphago-alphazero-taught-itself-to-play-three-different-games/
69. Google's DeepMind —the group that brought you the champion game-playing AIs AlphaGo and AlphaGoZero—is back with a new, improved, and more-generalized version. Dubbed AlphaZero, this

https://www.technologynetworks.com/neuroscience/articles/deepminds-alphago-zero-learns-from-scratch-without-any-human-input-293412
AlphaGO Zero learned to play the game from scratch, with no human interaction. In their paper published in Nature, the researchers incorporated a single neural network and developed algorithms that resulted in rapid improvement and stable learning. Earlier versions used two neural networks, a "policy network" to select the next move and a

https://www.scientificamerican.com/article/ai-versus-ai-self-taught-alphago-zero-vanquishes-its-predecessor/
AlphaGo Zero even devised its own unconventional strategies. The game go is typically played using "stones" colored either black or white on a board with a 19 by 19 grid.

https://deepmind.google/technologies/alphago/
AlphaGo's 4-1 victory in Seoul, South Korea, in March 2016 was watched by over 200 million people worldwide. This landmark achievement was a decade ahead of its time. Watch ... AlphaZero and MuZero. Powerful, general AI systems that mastered a range of board games and video games — and are now helping us solve real-world problems. Research.

https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0
Download the AlphaGo Zero cheat sheet. Update! (2nd December 2019) I've just released a series on MuZero — AlphaZero's younger and cooler brother.

https://www.chess.com/news/view/how-alphazero-learns-chess
English‎. AlphaZero's learning process is, to some extent, similar to that of humans. A new paper from DeepMind, which includes a contribution from the 14th world chess champion Vladimir Kramnik, provides strong evidence for the existence of human-understandable concepts in AlphaZero's network, even though AlphaZero has never seen a human

https://paperswithcode.com/paper/understanding-generalizing-alphago-zero
Understanding & Generalizing AlphaGo Zero. AlphaGo Zero (AGZ) introduced a new {\em tabula rasa} reinforcement learning algorithm that has achieved superhuman performance in the games of Go, Chess, and Shogi with no prior knowledge other than the rules of the game. This success naturally begs the question whether it is possible to develop

https://deepmind.google/technologies/alphazero-and-muzero/
MuZero: AI that can plan. MuZero goes one step further than AlphaZero. Without being told the rules of any game, MuZero matches AlphaZero's level of performance in Go, chess and shogi, and also learns to master a suite of visually complex Atari games. It does this by learning a model of its environment, such as the game it's playing.

https://www.technologyreview.com/2017/12/06/105016/deepminds-groundbreaking-alphago-zero-ai-is-now-a-versatile-gamer/
Now, in a paper published on arXiv, the DeepMind team reports that the software has been generalized so that it can learn other games. It describes two new examples in which AlphaGo Zero was

https://www.datasciencecentral.com/deep-learning-alphago-zero-explained-in-one-picture/
Recently Google DeepMind announced AlphaGo Zero — an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the highly complex and challenging domain of Go, 'tabula rasa' — that is, from a blank slate, with no human expert play used as training data. It thrashed the previous reincarnation 100-0, using… Read More »Deep Learning

https://mashable.com/article/alphazero-ai-neuralnet-chess-player
AlphaZero comes after many years of research, succeeding AlphaGo Zero from last year, the world's best Go player. But this time around there wasn't any human help. AlphaZero taught itself how to