TL;DR We introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model.

Install, then try our pretrained world models!

python src/play.py --pretrained

Autoregressive imagination with DIAMOND on a subset of Atari games

• Installation
• Try our playable diffusion world models
• Launch a training run
• Configuration
• Visualization
  • Play mode (default)
  • Dataset mode (add -d)
  • Other options, common to play/dataset modes
• Run folder structure
• Results
• Citation
• Credits

Clone this repository:

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

We recommend using miniconda to create a new environment:

conda create -n diamond python=3.10
conda activate diamond

Install dependencies listed in 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.

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 🤗 and cached on your machine.

First things you might want to try:

• Press m to take control (the policy is playing by default).
• Press ↑ to increase the imagination horizon (default is 15, which is frustrating when playing yourself).

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 for more details about the available commands and options.

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

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:

./scripts/resume.sh

We use 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 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.

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

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.

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_<env_name>_<controller>. 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.

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

python src/play.py -d

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

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

--fps FPS             Target frame rate (default 15).
--size SIZE           Window size (default 800).
--no-header           Remove header.

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

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
    |   ...

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

TODO

• https://github.com/crowsonkb/k-diffusion/
• https://github.com/huggingface/huggingface_hub
• https://github.com/google-research/rliable
• https://github.com/pytorch/pytorch