Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/eloialonso/iris
Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
https://github.com/eloialonso/iris
artificial-intelligence atari deep-learning machine-learning reinforcement-learning research transformers world-models
Last synced: 3 months ago
JSON representation
Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
- Host: GitHub
- URL: https://github.com/eloialonso/iris
- Owner: eloialonso
- License: gpl-3.0
- Created: 2022-08-23T07:59:35.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-27T19:56:27.000Z (9 months ago)
- Last Synced: 2024-07-28T17:38:31.294Z (3 months ago)
- Topics: artificial-intelligence, atari, deep-learning, machine-learning, reinforcement-learning, research, transformers, world-models
- Language: Python
- Homepage: https://openreview.net/forum?id=vhFu1Acb0xb
- Size: 13.8 MB
- Stars: 782
- Watchers: 22
- Forks: 76
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - eloialonso/iris
README
# Transformers are Sample-Efficient World Models (IRIS)
[Transformers are Sample-Efficient World Models](https://openreview.net/forum?id=vhFu1Acb0xb)
[Vincent Micheli](https://vmicheli.github.io)\*, [Eloi Alonso](https://eloialonso.github.io)\*, [François Fleuret](https://fleuret.org/francois/)
\* Denotes equal contribution
IRIS agent after 100k environment steps, i.e. two hours of real-time experience
**tl;dr**
- IRIS is a data-efficient agent trained over millions of imagined trajectories in a world model.
- The world model is composed of a discrete autoencoder and an autoregressive Transformer.
- Our approach casts dynamics learning as a sequence modeling problem, where the autoencoder builds a language of image tokens and the Transformer composes that language over time.
## BibTeX
If you find this code or paper useful, please use the following reference:
```
@inproceedings{
iris2023,
title={Transformers are Sample-Efficient World Models},
author={Vincent Micheli and Eloi Alonso and Fran{\c{c}}ois Fleuret},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=vhFu1Acb0xb}
}
```
## Setup
- Install [PyTorch](https://pytorch.org/get-started/locally/) (torch and torchvision). Code developed with torch==1.11.0 and torchvision==0.12.0.
- Install [other dependencies](requirements.txt): `pip install -r requirements.txt`
- Warning: Atari ROMs will be downloaded with the dependencies, which means that you acknowledge that you have the license to use them.
## Launch a training run
```bash
python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0 wandb.mode=online
```
By default, the logs are synced to [weights & biases](https://wandb.ai), set `wandb.mode=disabled` to turn it off.
## Configuration
- All configuration files are located in `config/`, the main configuration file is `config/trainer.yaml`.
- The simplest way to customize the configuration is to edit these files directly.
- Please refer to [Hydra](https://github.com/facebookresearch/hydra) for more details regarding configuration management.
## Run folder
Each new run is located at `outputs/YYYY-MM-DD/hh-mm-ss/`. This folder is structured as:
```txt
outputs/YYYY-MM-DD/hh-mm-ss/
│
└─── checkpoints
│ │ last.pt
| | optimizer.pt
| | ...
│ │
│ └─── dataset
│ │ 0.pt
│ │ 1.pt
│ │ ...
│
└─── config
│ | trainer.yaml
|
└─── media
│ │
│ └─── episodes
│ | │ ...
│ │
│ └─── reconstructions
│ | │ ...
│
└─── scripts
| | eval.py
│ │ play.sh
│ │ resume.sh
| | ...
|
└─── src
| | ...
|
└─── wandb
| ...
```
- `checkpoints`: contains the last checkpoint of the model, its optimizer and the dataset.
- `media`:
- `episodes`: contains train / test / imagination episodes for visualization purposes.
- `reconstructions`: contains original frames alongside their reconstructions with the autoencoder.
- `scripts`: **from the run folder**, you can use the following three scripts.
- `eval.py`: Launch `python ./scripts/eval.py` to evaluate the run.
- `resume.sh`: Launch `./scripts/resume.sh` to resume a training that crashed.
- `play.sh`: Tool to visualize some interesting aspects of the run.
- Launch `./scripts/play.sh` to watch the agent play live in the environment. If you add the flag `-r`, the left panel displays the original frame, the center panel displays the same frame downscaled to the input resolution of the discrete autoencoder, and the right panel shows the output of the autoencoder (what the agent actually sees).
- Launch `./scripts/play.sh -w` to unroll live trajectories with your keyboard inputs (i.e. to play in the world model). Note that for faster interaction, the memory of the Transformer is flushed every 20 frames.
- Launch `./scripts/play.sh -a` to watch the agent play live in the world model. Note that for faster interaction, the memory of the Transformer is flushed every 20 frames.
- Launch `./scripts/play.sh -e` to visualize the episodes contained in `media/episodes`.
- Add the flag `-h` to display a header with additional information.
- Press '`,`' to start and stop recording. The corresponding segment is saved in `media/recordings` in mp4 and numpy formats.
- Add the flag `-s` to enter 'save mode', where the user is prompted to save trajectories upon completion.
## Results notebook
The folder `results/data/` contains raw scores (for each game, and for each training run) for IRIS and the baselines.
Use the notebook `results/results_iris.ipynb` to reproduce the figures from the paper.
## Pretrained models
Pretrained models are available [here](https://github.com/eloialonso/iris_pretrained_models).
- To start a training run from one of these checkpoints, in the section `initialization` of `config/trainer.yaml`, set `path_to_checkpoint` to the corresponding path, and `load_tokenizer`, `load_world_model`, and `load_actor_critic` to `True`.
- To visualize one of these checkpoints, set `train.id` to the corresponding game in `config/env/default.yaml`, create a `checkpoints` directory and copy the checkpoint to `checkpoints/last.pt`. You can then visualize the agent with `./scripts/play.sh` as described above.
## Credits
- [https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch)
- [https://github.com/CompVis/taming-transformers](https://github.com/CompVis/taming-transformers)
- [https://github.com/karpathy/minGPT](https://github.com/karpathy/minGPT)
- [https://github.com/google-research/rliable](https://github.com/google-research/rliable)