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AlphaGo earned positive reviews. On Rotten Tomatoes, the film has an approval rating of 100%, with an average score of 8/10, based on 10 reviews. [4] Charlotte O'Sullivan of Evening Standard gave the film 4 stars out of five, calling it a "gripping, emotional documentary, which gets us thinking, about thinking, in a whole new way".
"Though having watched only a few matches, we received the impression that AlphaGo Zero plays more like a human than its predecessors," Mok said. [21] Chinese Go professional Ke Jie commented on the remarkable accomplishments of the new program: "A pure self-learning AlphaGo is the strongest. Humans seem redundant in front of its self-improvement."
Decommissioned AlphaGo backend rack. Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alpha–beta pruning, tree traversal and heuristic search.
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
He studied at Christ's College, Cambridge, [3] graduating in 1997 with the Addison-Wesley award, and having befriended Demis Hassabis whilst at Cambridge. [4] Silver returned to academia in 2004 at the University of Alberta to study for a PhD on reinforcement learning, [5] where he co-introduced the algorithms used in the first master-level 9×9 Go programs and graduated in 2009.
AlphaDev is an artificial intelligence system developed by Google DeepMind to discover enhanced computer science algorithms using reinforcement learning.AlphaDev is based on AlphaZero, a system that mastered the games of chess, shogi and go by self-play.
AlphaGo won the final match two days later. [27] [28] With this victory, AlphaGo became the first program to beat a 9 dan human professional in a game without handicaps on a full-sized board. In May 2017, AlphaGo beat Ke Jie, who at the time was ranked top in the world, [29] [30] in a three-game match during the Future of Go Summit. [31]
MuZero (MZ) is a combination of the high-performance planning of the AlphaZero (AZ) algorithm with approaches to model-free reinforcement learning. The combination allows for more efficient training in classical planning regimes, such as Go, while also handling domains with much more complex inputs at each stage, such as visual video games.