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Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". Intuitively, agents learn to improve their performance by playing "against themselves".
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.
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.
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 ...
AlphaZero is a generic reinforcement learning algorithm – originally devised for the game of go – that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules."
It learned how to play chess through reinforcement learning from repeated self-play, using a distributed computing network coordinated at the Leela Chess Zero website. However, as of November 2024 most models used by the engine are trained through supervised learning on data generated by previous reinforcement learning runs. [2]
Pages for logged out editors learn more. Contributions; Talk; Self-play (reinforcement learning technique)
With zero knowledge built in, the network learned to play the game at an intermediate level by self-play and TD(). Seminal textbooks by Sutton and Barto on reinforcement learning, [6] Bertsekas and Tsitiklis on neuro-dynamic programming, [7] and others [8] advanced knowledge and interest in the field.