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🆕 Introducing JAT, the first open-source multi-modal, multi-task multi-domain agent! 🤖 A step toward open generalist agents! 🚀 📰 Blog:

73,212 次观看 • 2 年前 •via X (Twitter)

10 条评论

Quentin Gallouédec 的头像
Quentin Gallouédec2 年前

Huge kudos to @ClementRomac @edwardbeeching @Thom_Wolf for all the work you've done.

Quentin Gallouédec 的头像
Quentin Gallouédec2 年前

- 📄 Paper: - 💻 Code: - 🗂️ Dataset: - 🤖 Model:

Jeff Clune 的头像
Jeff Clune2 年前

Very cool! Did you consider using Go-Explore for the Atari trajectories? It solves all Atari games.

Quentin Gallouédec 的头像
Quentin Gallouédec2 年前

Go-Explore is definitely a golden choice for Atari. Having pre-trained Go-Explore agents and a trajectory dataset hosted of 🤗 Hub would allow JAT to be trained straight away with the Go-Explore data!

Bryan Kyritz 的头像
Bryan Kyritz2 年前

This video is so insane

Yutao Chen 的头像
Yutao Chen2 年前

Does JAT use the same network and same weight for all tasks (i.e. atari, mujuco, minigrid)? That's genuinely fascinating! I'm working on similar things recently, i.e. learning a world model to train multi-task RL agents, and your work looks really promising and inspiring 🤗

Quentin Gallouédec 的头像
Quentin Gallouédec2 年前

Yes, same weights for all tasks!

Ruben Hassid 的头像
Ruben Hassid2 年前

But all I want is a multi-fruit juice

Matt Shumer 的头像
Matt Shumer2 年前

@josh_bickett

Nicolay Rusnachenko 的头像
Nicolay Rusnachenko2 年前

Thanks for sharing and making aware of it! 👏👀

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