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Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather than having some rare human-programmed edge cases to help recognize unusual Go board positions. The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game's outcome. [10]
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.
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.
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.
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.
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]
AlphaGo used two deep neural networks: a policy network to evaluate move probabilities and a value network to assess positions. The policy network trained via supervised learning, and was subsequently refined by policy-gradient reinforcement learning. The value network learned to predict winners of games played by the policy network against itself.
Google's 2015 AlphaGo was the first AI agent to beat a professional Go player. [5] AlphaGo used a deep learning model to train the weights of a Monte Carlo tree search (MCTS). The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance ...