Powerful Dota 2 With Large Scale Deep Reinforcement Learning

Posted on Dec 24

• Originally published at paperium.net

In 2019 a computer system called OpenAI Five beat the best human team at Dota 2. The game is long, messy and full of hidden moves so it was a big surprise. Instead of teaching rules, the team let the program learn by playing itself again and again, practicing non stop for months. They built tools so it could improve while it played, and that steady practice made the system better than people at fast teamwork and planning. This win showed that self-play can reach skills humans thought only people could hold. It wasnt about magic, more about lots of tries and smart training. Watching the AI learn felt a bit like watching a new player grow up, making choices, then getting faster and smarter. The moment it beat the world champions changed how many think about games and machines. What comes next nobody knows but it's clear machines can now reach superhuman levels at some hard tasks, and that idea is both exciting and a little strange.

Read article comprehensive review in Paperium.net: Dota 2 with Large Scale Deep Reinforcement Learning

🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.

Templates let you quickly answer FAQs or store snippets for re-use.

Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink.

For further actions, you may consider blocking this person and/or reporting abuse

Source: Dev.to