Dota 2 with Large Scale Deep Reinforcement Learning

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.

Comments

There's unfortunately not much to read here yet...

Discover the Best of Machine Learning.

Ever having issues keeping up with everything that's going on in Machine Learning? That's where we help. We're sending out a weekly digest, highlighting the Best of Machine Learning.

Join over 700 Machine Learning Engineers receiving our weekly digest.

Best of Machine LearningBest of Machine Learning

Discover the best guides, books, papers and news in Machine Learning, once per week.

Twitter