Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, null, :, null, Berner, Christopher, Brockman, Greg, Chan, Brooke, Cheung, Vicki, Dębiak, Przemysław, Dennison, Christy, Farhi, David, Fischer, Quirin, Hashme, Shariq, Hesse, Chris, Józefowicz, Rafal, Gray, Scott, Olsson, Catherine, Pachocki, Jakub, Petrov, Michael, Pinto, Henrique Pondé de Oliveira, Raiman, Jonathan, Salimans, Tim, Schlatter, Jeremy, Schneider, Jonas, Sidor, Szymon, Sutskever, Ilya, Tang, Jie, Wolski, Filip, Zhang, Susan
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.
Dec-13-2019
- Country:
- North America
- United States
- Washington > King County
- Seattle (0.04)
- Oregon > Lane County
- Eugene (0.04)
- New York > New York County
- New York City (0.04)
- Hawaii > Honolulu County
- Honolulu (0.04)
- Georgia > Fulton County
- Atlanta (0.04)
- Florida > Broward County
- Fort Lauderdale (0.04)
- California > San Francisco County
- San Francisco (0.14)
- Washington > King County
- Puerto Rico > San Juan
- San Juan (0.04)
- United States
- Asia > Middle East
- Jordan (0.04)
- North America
- Genre:
- Research Report > New Finding (0.45)
- Industry:
- Information Technology (1.00)
- Leisure & Entertainment
- Sports (1.00)
- Games > Computer Games (1.00)
- Technology: