45 lines
1.7 KiB
Markdown
45 lines
1.7 KiB
Markdown
# dreamerv3-torch
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Pytorch implementation of [Mastering Diverse Domains through World Models](https://arxiv.org/abs/2301.04104v1). DreamerV3 is a scalable algorithm that outperforms previous approaches across various domains with fixed hyperparameters.
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## Instructions
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Get dependencies:
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```
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pip install -r requirements.txt
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```
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Train the agent on Walker Walk in DMC Vision:
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```
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python3 dreamer.py --configs dmc_vision --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk
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```
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Train the agent on Walker Walk in DMC Proprio:
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```
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python3 dreamer.py --configs dmc_proprio --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk
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```
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Train the agent on Alien in Atari 100K:
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```
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python3 dreamer.py --configs atari100k --task atari_alien --logdir ./logdir/atari_alien
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```
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Monitor results:
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```
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tensorboard --logdir ~/dreamerv3-torch/logdir
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```
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## Results
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#### DMC Vision
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#### Atari 100k
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#### DMC Proprio
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## Acknowledgments
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This code is heavily inspired by the following works:
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- danijar's Dreamer-v3 jax implementation: https://github.com/danijar/dreamerv3
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- danijar's Dreamer-v2 tensorflow implementation: https://github.com/danijar/dreamerv2
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- jsikyoon's Dreamer-v2 pytorch implementation: https://github.com/jsikyoon/dreamer-torch
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- RajGhugare19's Dreamer-v2 pytorch implementation: https://github.com/RajGhugare19/dreamerv2
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- denisyarats's DrQ-v2 original implementation: https://github.com/facebookresearch/drqv2
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