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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 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 10

Фото профиля Quentin Gallouédec
Quentin Gallouédec2 лет назад

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

Фото профиля Quentin Gallouédec
Quentin Gallouédec2 лет назад

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

Фото профиля Jeff Clune
Jeff Clune2 лет назад

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

Фото профиля Quentin Gallouédec
Quentin Gallouédec2 лет назад

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!

Фото профиля Bryan Kyritz
Bryan Kyritz2 лет назад

This video is so insane

Фото профиля Yutao Chen
Yutao Chen2 лет назад

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 🤗

Фото профиля Quentin Gallouédec
Quentin Gallouédec2 лет назад

Yes, same weights for all tasks!

Фото профиля Ruben Hassid
Ruben Hassid2 лет назад

But all I want is a multi-fruit juice

Фото профиля Matt Shumer
Matt Shumer2 лет назад

@josh_bickett

Фото профиля Nicolay Rusnachenko
Nicolay Rusnachenko2 лет назад

Thanks for sharing and making aware of it! 👏👀

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