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https://github.com/eloialonso/diamond

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.
https://github.com/eloialonso/diamond

artifical-intelligense atari deep-learning diffusion-models machine-learning reinforcement-learning research world-models

Last synced: 2 months ago
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DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.

Host: GitHub
URL: https://github.com/eloialonso/diamond
Owner: eloialonso
License: mit
Created: 2024-05-19T22:31:40.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-08-21T16:17:06.000Z (4 months ago)
Last Synced: 2024-08-21T18:20:16.077Z (4 months ago)
Topics: artifical-intelligense, atari, deep-learning, diffusion-models, machine-learning, reinforcement-learning, research, world-models
Language: Python
Homepage: https://arxiv.org/abs/2405.12399
Size: 12.2 MB
Stars: 197
Watchers: 4
Forks: 9
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # Diffusion for World Modeling: Visual Details Matter in Atari (NeurIPS 2024 Spotlight)

[**TL;DR**] 💎 DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained entirely in a diffusion world model.

🌍 [Project Page](https://diamond-wm.github.io) • 🤓 [Paper](https://arxiv.org/pdf/2405.12399) • 𝕏 [Atari thread](https://x.com/EloiAlonso1/status/1793916382779982120) • 𝕏 [CSGO thread](https://x.com/EloiAlonso1/status/1844803606064611771)



  RL agent playing in autoregressive imagination of Atari world models

  


  





  Human player in CSGO world model (full quality video here)

  


  



Quick install to try our [pretrained world models](#try) using [miniconda](https://docs.anaconda.com/free/miniconda/miniconda-install/):

>```bash

>git clone [email protected]:eloialonso/diamond.git

>cd diamond

>conda create -n diamond python=3.10

>conda activate diamond

>pip install -r requirements.txt

>```

For Atari (world model + RL agent)

>```bash

>python src/play.py --pretrained

>```

For CSGO (world model only)

>```bash

>git checkout csgo

>python src/play.py

>```

And press `m` to take control (the policy is playing by default)!

**Warning**: Atari ROMs will be downloaded with the dependencies, which means that you acknowledge that you have the license to use them.

## CSGO

**Edit**: Check out the [csgo branch](https://github.com/eloialonso/diamond/tree/csgo) to try our DIAMOND's world model trained on *Counter-Strike: Global Offensive*!

```bash

git checkout csgo

python src/play.py

```

> Note on Apple Silicon you must enable CPU fallback for MPS backend with

> PYTORCH_ENABLE_MPS_FALLBACK=1 python src/play.py



## Quick Links

- [Try our playable diffusion world models](#try)

- [Launch a training run](#launch)

- [Configuration](#configuration)

- [Visualization](#visualization)

  - [Play mode (default)](#play_mode)

  - [Dataset mode (add `-d`)](#dataset_mode)

  - [Other options, common to play/dataset modes](#other_options)

- [Run folder structure](#structure)

- [Results](#results)

- [Citation](#citation)

- [Credits](#credits)



## [⬆️](#quick_links) Try our playable diffusion world models

```bash

python src/play.py --pretrained

```

Then select a game, and world model and policy pretrained on Atari 100k will be downloaded from our [repository on Hugging Face Hub 🤗](https://huggingface.co/eloialonso/diamond) and cached on your machine.

Some things you might want to try:

- Press `m` to change the policy between the agent and human (the policy is playing by default).

- Press `↑/↓` to change the imagination horizon (default is 50 for playing).

To adjust the sampling parameters (number of denoising steps, stochasticity, order, etc) of the trained diffusion world model, for instance to trade off sampling speed and quality, edit the section `world_model_env.diffusion_sampler` in the file `config/trainer.yaml`.

See [Visualization](#visualization) for more details about the available commands and options.



## [⬆️](#quick_links) Launch a training run

To train with the hyperparameters used in the paper, launch:

```bash

python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0

```

This creates a new folder for your run, located in `outputs/YYYY-MM-DD/hh-mm-ss/`.

To resume a run that crashed, navigate to the fun folder and launch:

```bash

./scripts/resume.sh

```



## [⬆️](#quick_links) Configuration

We use [Hydra](https://github.com/facebookresearch/hydra) for configuration management.

All configuration files are located in the `config` folder:

- `config/trainer.yaml`: main configuration file.

- `config/agent/default.yaml`: architecture hyperparameters.

- `config/env/atari.yaml`: environment hyperparameters.

You can turn on logging to [weights & biases](https://wandb.ai) in the `wandb` section of `config/trainer.yaml`.

Set `training.model_free=true` in the file `config/trainer.yaml` to "unplug" the world model and perform standard model-free reinforcement learning.



## [⬆️](#quick_links) Visualization



### [⬆️](#quick_links) Play mode (default)

To visualize your last checkpoint, launch **from the run folder**:

```bash

python src/play.py

```

By default, you visualize the policy playing in the world model. To play yourself, or switch to the real environment, use the controls described below.

```txt

Controls (play mode)

(Game-specific commands will be printed on start up)

⏎   : reset environment

m   : switch controller (policy/human)

↑/↓ : imagination horizon (+1/-1)

←/→ : next environment [world model ←→ real env (test) ←→ real env (train)]

.   : pause/unpause

e   : step-by-step (when paused)

```

Add `-r` to toggle "recording mode" (works only in play mode). Every completed episode will be saved in `dataset/rec__`. For instance:

- `dataset/rec_wm_π`: Policy playing in world model.

- `dataset/rec_wm_H`: Human playing in world model.

- `dataset/rec_test_H`: Human playing in test real environment.

You can then use the "dataset mode" described in the next section to replay the stored episodes.



### [⬆️](#quick_links) Dataset mode (add `-d`)

**In the run folder**, to visualize the datasets contained in the `dataset` subfolder, add `-d` to switch to "dataset mode":

```bash

python src/play.py -d

```

You can use the controls described below to navigate the datasets and episodes.

```txt

Controls (dataset mode)

m   : next dataset (if multiple datasets, like recordings, etc)

↑/↓ : next/previous episode

←/→ : next/previous timestep in episodes

PgUp: +10 timesteps

PgDn: -10 timesteps

⏎   : back to first timestep

```



### [⬆️](#quick_links) Other options, common to play/dataset modes

```txt

--fps FPS             Target frame rate (default 15).

--size SIZE           Window size (default 800).

--no-header           Remove header.

```



## [⬆️](#quick_links) Run folder structure

Each new run is located at `outputs/YYYY-MM-DD/hh-mm-ss/`. This folder is structured as follows:

```txt

outputs/YYYY-MM-DD/hh-mm-ss/

│

└─── checkpoints

│   │   state.pt  # full training state

│   │

│   └─── agent_versions

│       │   ...

│       │   agent_epoch_00999.pt

│       │   agent_epoch_01000.pt  # agent weights only

│

└─── config

│   |   trainer.yaml

|

└─── dataset

│   │

│   └─── train

│   |   │   info.pt

│   |   │   ...

|   |

│   └─── test

│       │   info.pt

│       │   ...

│

└─── scripts

│   │   resume.sh

|   |   ...

|

└─── src

|   |   main.py

|   |   ...

|

└─── wandb

    |   ...

```



## [⬆️](#quick_links) Results

The file [results/data/DIAMOND.json](results/data/DIAMOND.json) contains the results for each game and seed used in the paper.

The DDPM code used for Section 5.1 of the paper can be found on the [ddpm](https://github.com/eloialonso/diamond/tree/ddpm) branch.



## [⬆️](#quick-links) Citation

```text

@inproceedings{alonso2024diffusionworldmodelingvisual,

      title={Diffusion for World Modeling: Visual Details Matter in Atari},

      author={Eloi Alonso and Adam Jelley and Vincent Micheli and Anssi Kanervisto and Amos Storkey and Tim Pearce and François Fleuret},

      booktitle={Thirty-eighth Conference on Neural Information Processing Systems}}

      year={2024},

      url={https://arxiv.org/abs/2405.12399},

}

```



## [⬆️](#quick_links) Credits

- [https://github.com/crowsonkb/k-diffusion/](https://github.com/crowsonkb/k-diffusion/)

- [https://github.com/huggingface/huggingface_hub](https://github.com/huggingface/huggingface_hub)

- [https://github.com/google-research/rliable](https://github.com/google-research/rliable)

- [https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch)