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Self-play is used by the AlphaZero program to improve its performance in the games of chess, shogi and go. [2] Self-play is also used to train the Cicero AI system to outperform humans at the game of Diplomacy. The technique is also used in training the DeepNash system to play the game Stratego. [3] [4]
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.
In 100 shogi games against Elmo (World Computer Shogi Championship 27 summer 2017 tournament version with YaneuraOu 4.73 search), AlphaZero won 90 times, lost 8 times and drew twice. [11] As in the chess games, each program got one minute per move, and Elmo was given 64 threads and a hash size of 1 GB. [2]
General video game playing (GVGP) is the concept of GGP adjusted to the purpose of playing video games. For video games, game rules have to be either learnt over multiple iterations by artificial players like TD-Gammon , [ 5 ] or are predefined manually in a domain-specific language and sent in advance to artificial players [ 6 ] [ 7 ] like in ...
TD-Gammon is a computer backgammon program developed in 1992 by Gerald Tesauro at IBM's Thomas J. Watson Research Center.Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically TD-Lambda.
The 2014 research paper on "Variational Recurrent Auto-Encoders" attempted to generate music based on songs from 8 different video games. This project is one of the few conducted purely on video game music. The neural network in the project was able to generate data that was very similar to the data of the games it trained off of. [35]
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.
One traditional AI technique for creating game playing software is to use a minimax tree search. This involves playing out all hypothetical moves on the board up to a certain point, then using an evaluation function to estimate the value of that position for the current player. The move which leads to the best hypothetical board is selected ...