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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".
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 ...
Pages for logged out editors learn more. Contributions; Talk; Self-play (reinforcement learning technique)
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."
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.
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.
Unlike the jury in Google's trial in San Francisco, U.S. District Judge Yvonne Gonzalez-Rogers largely sided with Apple in an 185-decision that defined the Play Store and Apple's iPhone app store ...
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .