Pytorch implementation of Mastering Diverse Domains through World Models. DreamerV3 is a scalable algorithm that outperforms previous approaches across various domains with fixed hyperparameters.

Instructions

Get dependencies:

pip install -r requirements.txt

Train the agent on Walker Walk in DMC Vision:

python3 dreamer.py --configs dmc_vision --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk

Train the agent on Walker Walk in DMC Proprio:

python3 dreamer.py --configs dmc_proprio --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk

Train the agent on Alien in Atari 100K:

python3 dreamer.py --configs atari100k --task atari_alien --logdir ./logdir/atari_alien

Train the agent on Crafter:

python3 dreamer.py --configs crafter --logdir ./logdir/crafter

Monitor results:

tensorboard --logdir ./logdir

Results

DMC Vision

Atari 100k

DMC Proprio

Acknowledgments

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