Full Paper - Accelerating Self-Play Learning in Go скачать в хорошем качестве

Full Paper - Accelerating Self-Play Learning in Go

This is a full reading of the paper: Accelerating Self-Play Learning in Go By introducing several improvements to the AlphaZero process and architecture, this paper greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, the KataGo bot only learns from neural-net-guided Monte Carlo tree search self-play. But whereas AlphaZero required thousands of TPUs over several days and ELF required thousands of GPUs over two weeks, KataGo surpasses ELF’s final model after only 19 days on fewer than 30 GPUs. Much of the speedup involves non-domain-specific improvements that might directly transfer to other problems. Further gains from domain-specific techniques reveal the remaining efficiency gap between the best methods and purely general methods such as AlphaZero. ----------------- You can find the full paper at: https://arxiv.org/abs/1902.10565 Note the paper has an appendix section which isn't covered in this video due to its length. The paper’s author is David J. Wu ----------------- About the Full Papers youtube channel: Reading research papers takes a lot of time. This channel makes it easier. You might enjoy this channel if you: Prefer to learn through hearing rather than reading alone. Like to take a first-pass through a paper while commuting. Spend long days staring at a screen and want to give your eyes a rest, lay back and hear a research paper at the end of the day. Have a backlog of research papers to read and would like to catch up by reading some of them more casually. Want to take a first-pass through a paper before deciding if it’s worth sitting down and working through the details. I tend to post papers I’m interested in, but if you have a paper you’d like me to generate a video for just send me a message on twitter at   / marcsto   or email me at [email protected] and I should be able to help. This video was created by Marc Stogaitis. Just to make sure it’s clear, I did not write the paper, I’m just trying to help you learn it. You can visit my website at https://marc.ai