AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |
Back to Blog
Solving runix cube3/13/2023 ![]() The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchmark set of such applications. The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. © 2017 Macmillan Publishers Limited, part of Springer Nature. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. ![]() The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. ![]() Recently, AlphaGo became the first program to defeat a world champion in the game of Go. A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains.
0 Comments
Read More
Leave a Reply. |