55 lines
2.5 KiB
Markdown
55 lines
2.5 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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### Method 1: Manual
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Get dependencies with python 3.11:
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```
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pip install -r requirements.txt
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```
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Run training on 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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Monitor results:
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```
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tensorboard --logdir ./logdir
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```
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To set up Atari or Minecraft environments, please check the scripts located in [env/setup_scripts](https://github.com/NM512/dreamerv3-torch/tree/main/envs/setup_scripts).
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### Method 2: Docker
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Please refer to the Dockerfile for the instructions, as they are included within.
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## Benchmarks
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So far, the following benchmarks can be used for testing.
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| Environment | Observation | Action | Budget | Description |
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|-------------------|---|---|---|-----------------------|
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| [DMC Proprio](https://github.com/deepmind/dm_control) | State | Continuous | 500K | DeepMind Control Suite with low-dimensional inputs. |
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| [DMC Vision](https://github.com/deepmind/dm_control) | Image | Continuous |1M| DeepMind Control Suite with high-dimensional images inputs. |
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| [Atari 100k](https://github.com/openai/atari-py) | Image | Discrete |400K| 26 Atari games. |
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| [Crafter](https://github.com/danijar/crafter) | Image | Discrete |1M| Survival environment to evaluates diverse agent abilities.|
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| [Minecraft](https://github.com/minerllabs/minerl) | Image and State |Discrete |100M| Vast 3D open world.|
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| [Memory Maze](https://github.com/jurgisp/memory-maze) | Image |Discrete |100M| 3D mazes to evaluate RL agents' long-term memory.|
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## Results
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#### DMC Proprio
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#### DMC Vision
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#### Atari 100k
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#### Crafter
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<img src="https://github.com/NM512/dreamerv3-torch/assets/70328564/a0626038-53f6-4300-a622-7ac257f4c290" width="300" height="150" />
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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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