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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 Aufrufe • vor 2 Jahren •via X (Twitter)

10 Kommentare

Profilbild von Quentin Gallouédec
Quentin Gallouédecvor 2 Jahren

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

Profilbild von Quentin Gallouédec
Quentin Gallouédecvor 2 Jahren

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

Profilbild von Jeff Clune
Jeff Clunevor 2 Jahren

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

Profilbild von Quentin Gallouédec
Quentin Gallouédecvor 2 Jahren

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!

Profilbild von Bryan Kyritz
Bryan Kyritzvor 2 Jahren

This video is so insane

Profilbild von Yutao Chen
Yutao Chenvor 2 Jahren

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 🤗

Profilbild von Quentin Gallouédec
Quentin Gallouédecvor 2 Jahren

Yes, same weights for all tasks!

Profilbild von Ruben Hassid
Ruben Hassidvor 2 Jahren

But all I want is a multi-fruit juice

Profilbild von Matt Shumer
Matt Shumervor 2 Jahren

@josh_bickett

Profilbild von Nicolay Rusnachenko
Nicolay Rusnachenkovor 2 Jahren

Thanks for sharing and making aware of it! 👏👀

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