dreamerv3-torch/README.md

dreamerv3-torch

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

Monitor results:

tensorboard --logdir ~/dreamerv3-torch/logdir

Results

More results will be added in the future.

ToDo

• 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.

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