Dec 7, 2018 ¡¤ In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games.

Dec 5, 2017 ¡¤ In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains.

In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games.

Recently, the AlphaGo Zero algorithm achieved superhuman performance in the game of Go, by representing Go knowledge using deep convolutional neural networks (7 ...

Our results demonstrate that a general-purpose reinforcement learning algorithm can achieve, tabula rasa, superhuman performance across many challenging games.

Dec 6, 2017 ¡¤ In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains.

By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we ...

This paper generalizes the AlphaZero approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games.

Jan 23, 2019 ¡¤ AlphaZero is a general reinforcement learning algorithm. It performs exceptionally in all 3 games that it has been tested on: chess, shogi, and Go.

AlphaZero is a reinforcement learning agent for playing board games such as Go, chess, and shogi. Source: Mastering Chess and Shogi by Self-Play