开源围棋AI程序介绍和展望

Since we released ELF OpenGo last year,AI researchers have used the game-playing bot to better understand how AI systems learn,and Go enthusiasts have tested their skills against it as a new state-of-the-art artificial sparring partner.The open source bot has performed extremely well against humans including a 20-0 record against top professional Go players and has been widely adopted by the AI research community to run their own Go experiments and reproduce others results.ELF OpenGo has faced off against multiple modified versions of itself in AI-based Go tournaments.It has also played alongside humans,including as part of a U.S.Go Congress exhibition featuring mixed pairs each with one person and one ELF OpenGo system working together against another AI-human team.The Facebook AI Research(FAIR)team is now announcing new features and research,including an updated model that was retrained from results related to ELF OpenGoscratch.Were also releasing a Windows executable version of the bot,making it easier for Go players to use the system as a training aid,as well as a unique archive that shows ELF OpenGos analysis of 87,000 professional Go games.Present-day players can see how our system ranks the best pro players dating back to the 18th century,assessing their performance in detail,down to individual moves in specific games.Were excited that our development of this versatile platform is helping researchers better understand AI,and were gratified to see players in the Go community use it to hone their skills and study the game.I can definitely say that the ELF OpenGo project has brought a huge impact on the Korean Go community,”said Beomgeun Cho,Assistant Director of PR,Korea Baduk Association.“Since it came out,almost every competitive professional player in Korea has been using the ELF Go program to analyze their own and other players games.And because of that,not only has the level of Korean Go improved,but the level of the whole world has been improved significantly.”Making a powerful AI bot available to everyoneMaking a powerful AI bot available to everyoneWhen DeepMind published the results of its AlphaGo Zero bot in 2017,it demonstrated how useful the 4,000-year-old game of Go could be as a test bed for research related to deep reinforcement learning(RL).Due to its high-branching factors,convoluted interactions,and complicated patterns,effective Go bots must generalize to unseen and complicated situations,exploring and discovering new strategies.It provides an environment with millions of potential move combinations,but no hidden or chance-based game mechanics(such as rolling dice or shuffling playing cards).But while AlphaGo Zero and its successor,AlphaZero,have proved that AI systems can be trained to consistently beat human Go players,they function more as an aspirational example of deep RL than as a tool for the wider AI research community.As part of our commitment to open science,we released a reimplementation of AlphaZero last year,enabling other research labs to gain greater insight into the details for how these approaches work.The open-sourcing of our models also provides an essential benchmark for future research.We recognize that most researchers will not be able to reproduce our results even with the open sourced code due to the significant computational resources required.Thats why were sharing our insights based on retraining ELF OpenGo from scratch in a.This work sheds new new paperlight on why AI is so formidable against human players,and it also clarifies the technologys limitations,which could help researchers better understand the underlying mechanism and apply it to other situations.For the research community,our newly updated model and code represent the best version of ELF OpenGo yet,and by releasing our dataset of 20 million self-play games and the 1,500 intermediate models used to generate them,were further reducing the need for compute resources(self play being the most hardware-intensive component in the training process).And for researchers who want to dig deeper into how RL-based Go bots learn and play,our paper details the results of extensive ablation experiments,modifying individual features during evaluation to better understand the properties of these kinds of algorithms.Revealing the benefits and limitations of deep RLRevealing the benefits and limitations of deep RLThe key to ELF OpenGos strong performance is that it doesnt learn like humans do.The trial-and-error nature of deep RL where systems explore different moves,get both failure and success cases,and learn from them to take actions that lead to the latter might resemble human learning in a general sense,but the specific mechanics are very different.For example,ELF OpenGo may only learn from its knowledge that it won or lost a game that it played against itself.It doesnt know which particular moves had the greatest impact on whether it won or lost.Unlike human players,ELF OpenGo does not get advice from more s