Newt

Official release of Newt model checkpoints for the paper

Learning Massively Multitask World Models for Continuous Control by

Nicklas Hansen, Hao Su\*, Xiaolong Wang\* (UC San Diego)

Quick links: [Website] [Paper] [Dataset]

Model details

We open-source 200+ model checkpoints, including a multi-task Newt agent trained on 200 tasks simultaneously. We are excited to see what the community will do with these models, and hope that our release will encourage other research labs to open-source their checkpoints as well. This section aims to provide further details about the released models.

Model description

Developed by: Nicklas Hansen (UC San Diego)

Model type: TD-MPC2 and Newt models trained on tasks from MMBench (DMControl, Meta-World, Maniskill3, MiniArcade, Atari, and more).

License: MIT

Model sources

Repository: https://github.com/nicklashansen/newt

Paper: https://www.nicklashansen.com/NewtWM/newt.pdf

Uses

As one of the first major releases of model checkpoints for reinforcement learning, use of our TD-MPC2 and Newt checkpoints is fairly open-ended. We envision that our checkpoints will be useful for researchers interested in training, finetuning, evaluating, and analyzing single-task and multitask models on any of the 200 continuous control tasks that we release models for. However, we also expect the community to discover new use cases for these checkpoints.

Direct use

Model checkpoints can be loaded using the official implementation, and then be used to reproduce our results and/or generate trajectories for any of the supported tasks.

Out-of-scope use

We do not expect our model checkpoints to reliably generalize to new (unseen) tasks as is. Such model usage will most likely require some amount of fine-tuning on target task data.

How to get started with the models

Refer to the official implementation for installation instructions and example usage.

Citation

If you find our work useful, please consider citing the paper as follows:

BibTeX:

Contact

Correspondence to: Nicklas Hansen