Excited to share that DreamerV3 has been published in Nature!. Dreamer solves control tasks by imagining the future outcomes of its actions inside of a continuously learned world model 🌏. It's the first agent to find diamonds in Minecraft from scratch without human data! 💎. 👇

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Thank you @jeffclune!! 🙏.

Big thanks to my collaborators @jurgisp, @jimmybajimmyba, and @countzerozzz for making this project a reality ✨. Happy to answer any questions!.

News release:.

Nature article (open access):.

Dreamer masters new tasks without manual tweaking, making reinforcement learning broadly accessible for practitioners. I'm also really excited that it paves the way for building large world models of the physical world to make intelligent robots a reality! 🤖.

If you've already seen our earlier preprint, Dreamer now also significantly outperforms the best algorithms on ProcGen and surpasses TD-MPC2 on continuous control without action repeat (in faster wall-clock time). We also added a detailed description of the Minecraft setting.

Minecraft has been a substantial challenge in AI that requires exploring farsighted strategies from pixels and sparse rewards in an open world. Applied out of the box, Dreamer is the first algorithm to find diamonds in Minecraft without human data or curricula! 💎