Pytorch implementation of Mastering Diverse Domains through World Models. DreamerV3 is a scalable algorithm that outperforms previous approaches across various domains with fixed hyperparameters.
dreamerv3 on ARCLE
Get dependencies with python 3.9:
pip install -r requirements.txt
Run training on ARCLE:
python3 dreamer.py --configs arcle --task arcle_traj --logdir ./logdir/arlce
Monitor results:
tensorboard --logdir ./logdir
Please refer to the Dockerfile for the instructions, as they are included within.
This code is heavily inspired by the following works:
- danijar's Dreamer-v3 jax implementation: https://github.com/danijar/dreamerv3
- danijar's Dreamer-v2 tensorflow implementation: https://github.com/danijar/dreamerv2
- jsikyoon's Dreamer-v2 pytorch implementation: https://github.com/jsikyoon/dreamer-torch
- RajGhugare19's Dreamer-v2 pytorch implementation: https://github.com/RajGhugare19/dreamerv2
- denisyarats's DrQ-v2 original implementation: https://github.com/facebookresearch/drqv2
- NM512's Dreamer-v3 reimplmentation: https://github.com/NM512/dreamerv3-torch