Pytorch implementation of Mastering Diverse Domains through World Models. DreamerV3 is a scalable algorithm that outperforms previous approaches across various domains with fixed hyperparameters.
Get dependencies:
pip install -r requirements.txt
Train the agent on Walker Walk in Vision DMC:
python3 dreamer.py --configs defaults --task dmc_walker_walk --logdir ~/dreamerv3-torch/logdir/dmc_walker_walk
Train the agent on Alien in Atari 100K:
python3 dreamer.py --configs defaults atari100k --task atari_alien --logdir ~/dreamerv3-torch/logdir/atari_alien
Monitor results:
tensorboard --logdir ~/dreamerv3-torch/logdir
More results will be added in the future.
- Prototyping
- Modify implementation details based on the author's implementation
- Evaluate on DMC vision
- Evaluate on Atari 100K
- Add state input capability
- Evaluate on DMC Proprio
- etc.
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