← Flash PapersDota 2 with Large Scale Deep Reinforcement Learning — OpenAI Five

Share this Flash Paper

Dota 2 with Large Scale Deep Reinforcement Learning — OpenAI Five

https://flashpapers.ai/p/a67dxsnjgb
Make your own
OpenAI, : +25 more
Details ▸
Win rate vs Public:99.4%· OpenAI Five Arena (7,257 games)Match Score vs World Champions:2-0· Team OG ShowmatchOptimization Compute:770 +/- 50 PFlops/s-daysAverage Reaction Time:217ms· Dota 2
Abstract

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.