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Game-playing artificial intelligence

MuZero is a computer program developed by artificial intelligence research company DeepMind to master games without knowing their rules.[1][2][3] Its release in 2019 included benchmarks of its performance in go, chess, shogi, and a standard suite of Atari games. The algorithm uses an approach similar to AlphaZero. It matched AlphaZero's performance in chess and shogi, improved on its performance in Go (setting a new world record), and improved on the state of the art in mastering a suite of 57 Atari games (the Arcade Learning Environment), a visually-complex domain.

MuZero was trained via self-play, with no access to rules, opening books, or endgame tablebases. The trained algorithm used the same convolutional and residual algorithms as AlphaZero, but with 20% fewer computation steps per node in the search tree.[4]

MuZero really is discovering for itself how to build a model and understand it just from first principles.

On November 19, 2019, the DeepMind team released a preprint introducing MuZero.

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.

MuZero was derived directly from AZ code, sharing its rules for setting hyperparameters. Differences between the approaches include:[6]

The previous state of the art technique for learning to play the suite of Atari games was R2D2, the Recurrent Replay Distributed DQN.[7]

MuZero surpassed both R2D2's mean and median performance across the suite of games, though it did not do better in every game.

MuZero used 16 third-generation tensor processing units (TPUs) for training, and 1000 TPUs for selfplay for board games, with 800 simulations per step and 8 TPUs for training and 32 TPUs for selfplay for Atari games, with 50 simulations per step.

AlphaZero used 64 first-generation TPUs for training, and 5000 second-generation TPUs for selfplay. As TPU design has improved (third-generation chips are 2x as powerful individually as second-generation chips, with further advances in bandwidth and networking across chips in a pod), these are comparable training setups.

R2D2 was trained for 5 days through 2M training steps.

MuZero matched AlphaZero's performance in chess and Shogi after roughly 1 million training steps. It matched AZ's performance in Go after 500 thousand training steps and surpassed it by 1 million steps. It matched R2D2's mean and median performance across the Atari game suite after 500 thousand training steps and surpassed it by 1 million steps, though it never performed well on 6 games in the suite.

MuZero was viewed as a significant advancement over AlphaZero,[8] and a generalizable step forward in unsupervised learning techniques.[9][10] The work was seen as advancing understanding of how to compose systems from smaller components, a systems-level development more than a pure machine-learning development.[11]

While only pseudocode was released by the development team, Werner Duvaud produced an open source implementation based on that.[12]

MuZero has been used as a reference implementation in other work, for instance as a way to generate model-based behavior.[13]

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MuZero - Wikipedia