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[1912.06680] Dota 2 with Large Scale Deep Reinforcement Learning
paperarxiv.org·arxiv.org/abs/1912.06680
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[1912.06680] Dota 2 with Large Scale Deep Reinforcement Learning
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Computer Science > Machine Learning
arXiv:1912.06680 (cs)
[Submitted on 13 Dec 2019]
Title: Dota 2 with Large Scale Deep Reinforcement Learning
Authors: OpenAI : Christopher Berner , Greg Brockman , Brooke Chan , Vicki Cheung , Przemysław Dębiak , Christy Dennison , David Farhi , Quirin Fischer , Shariq Hashme , Chris Hesse , Rafal Józefowicz , Scott Gray , Catherine Olsson , Jakub Pachocki , Michael Petrov , Henrique P. d.O. Pinto , Jonathan Raiman , Tim Salimans , Jeremy Schlatter , Jonas Schneider , Szymon Sidor , Ilya Sutskever , Jie Tang , Filip Wolski , Susan Zhang View a PDF of the paper titled Dota 2 with Large Scale Deep Reinforcement Learning, by OpenAI: Christopher Berner and 24 other authors
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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.
Subjects:
Machine Learning (cs.LG) ; Machine Learning (stat.ML)
Cite as:
arXiv:1912.06680 [cs.LG]
(or
arXiv:1912.06680v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.1912.06680
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arXiv-issued DOI via DataCite
Submission history
From: Filip Wolski [ view email ]
[v1]
Fri, 13 Dec 2019 19:56:40 UTC (8,625 KB)
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