2.6 KiB
2.6 KiB
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
Run training on DMC Vision:
python3 dreamer.py --configs dmc_vision --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk
Monitor results:
tensorboard --logdir ./logdir
Benchmarks
So far, this repository allows testing the following benchmarks.
| Environment | Observation | Action | Description |
|---|---|---|---|
| DMC Proprio | State | Continuous | This benchmark contains 18 continuous control tasks with low-dimensional inputs and a budget of 500K environment steps. |
| DMC Vision | Image | Continuous | This benchmark consists of 20 continuous control tasks where the agent receives only high-dimensional images as inputs and a budget of 1M environment steps. |
| Atari 100k | Image | Discrete | This benchmark includes 26 Atari games and a budget of only 400K environment steps, amounting to 100K steps after action repeat or 2 hours of real time. |
| Crafter | Image | Discrete | This survival environment evaluates diverse agent abilities, including exploration, reasoning, credit assignment, and generalization. |
| Memory Maze | Image | Discrete | Memory Maze is a 3D benchmark with randomized mazes to evaluate RL agents' long-term memory. |
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