Training and visualizing of diffusion models from Planning with Diffusion for Flexible Behavior Synthesis.
The main branch contains code for training diffusion models and planning via value-function guided sampling on the D4RL locomotion environments. The kuka branch contains block-stacking experiments. The maze2d branch contains goal-reaching via inpainting in the Maze2D environments.
Updates
- 12/09/2022: Diffuser (the RL model) has been integrated into 🤗 Diffusers (the Hugging Face diffusion model library)! See these docs for how to run Diffuser using their pipeline.
- 10/17/2022: A bug in the value function scaling has been fixed in this commit. Thanks to Philemon Brakel for catching it!
Load a pretrained diffusion model and sample from it in your browser with scripts/diffuser-sample.ipynb.
conda env create -f environment.yml
conda activate diffuser
pip install -e .
Download pretrained diffusion models and value functions with:
./scripts/download_pretrained.sh
This command downloads and extracts a tarfile containing this directory to logs/pretrained. The models are organized according to the following structure:
└── logs/pretrained
├── ${environment_1}
│ ├── diffusion
│ │ └── ${experiment_name}
│ │ ├── state_${epoch}.pt
│ │ ├── sample-${epoch}-*.png
│ │ └── {dataset, diffusion, model, render, trainer}_config.pkl
│ ├── values
│ │ └── ${experiment_name}
│ │ ├── state_${epoch}.pt
│ │ └── {dataset, diffusion, model, render, trainer}_config.pkl
│ └── plans
│ └── defaults
│ ├── 0
│ ├── 1
│ ├── ...
│ └── 149
│
├── ${environment_2}
│ └── ...
The state_${epoch}.pt files contain the network weights and the config.pkl files contain the instantation arguments for the relevant classes.
The png files contain samples from different points during training of the diffusion model.
Within the plans subfolders, there are the results of 150 evaluation trials for each environment using the default hyperparameters.
To aggregate the results of the evaluations in the logs folder, run python scripts/read_results.py. (Expand to view the output of this command on the plans downloaded from Google Drive.)
hopper-medium-replay-v2 | defaults | logs/pretrained/hopper-medium-replay-v2/plans | 150 scores
93.6 +/- 0.37
hopper-medium-v2 | defaults | logs/pretrained/hopper-medium-v2/plans | 150 scores
74.3 +/- 1.36
hopper-medium-expert-v2 | defaults | logs/pretrained/hopper-medium-expert-v2/plans | 150 scores
103.3 +/- 1.30
walker2d-medium-replay-v2 | defaults | logs/pretrained/walker2d-medium-replay-v2/plans | 150 scores
70.6 +/- 1.60
walker2d-medium-v2 | defaults | logs/pretrained/walker2d-medium-v2/plans | 150 scores
79.6 +/- 0.55
walker2d-medium-expert-v2 | defaults | logs/pretrained/walker2d-medium-expert-v2/plans | 150 scores
106.9 +/- 0.24
halfcheetah-medium-replay-v2 | defaults | logs/pretrained/halfcheetah-medium-replay-v2/plans | 150 scores
37.7 +/- 0.45
halfcheetah-medium-v2 | defaults | logs/pretrained/halfcheetah-medium-v2/plans | 150 scores
42.8 +/- 0.32
halfcheetah-medium-expert-v2 | defaults | logs/pretrained/halfcheetah-medium-expert-v2/plans | 150 scores
88.9 +/- 0.25
To create the table of offline RL results from the paper, run python plotting/table.py. This will print a table that can be copied into a Latex document. (Expand to view table source.)
\definecolor{tblue}{HTML}{1F77B4}
\definecolor{tred}{HTML}{FF6961}
\definecolor{tgreen}{HTML}{429E9D}
\definecolor{thighlight}{HTML}{000000}
\newcolumntype{P}{>{\raggedleft\arraybackslash}X}
\begin{table*}[hb!]
\centering
\small
\begin{tabularx}{\textwidth}{llPPPPPPPPr}
\toprule
\multicolumn{1}{r}{\bf \color{black} Dataset} & \multicolumn{1}{r}{\bf \color{black} Environment} & \multicolumn{1}{r}{\bf \color{black} BC} & \multicolumn{1}{r}{\bf \color{black} CQL} & \multicolumn{1}{r}{\bf \color{black} IQL} & \multicolumn{1}{r}{\bf \color{black} DT} & \multicolumn{1}{r}{\bf \color{black} TT} & \multicolumn{1}{r}{\bf \color{black} MOPO} & \multicolumn{1}{r}{\bf \color{black} MOReL} & \multicolumn{1}{r}{\bf \color{black} MBOP} & \multicolumn{1}{r}{\bf \color{black} Diffuser} \\
\midrule
Medium-Expert & HalfCheetah & $55.2$ & $91.6$ & $86.7$ & $86.8$ & $95.0$ & $63.3$ & $53.3$ & $\textbf{\color{thighlight}105.9}$ & $88.9$ \scriptsize{\raisebox{1pt}{$\pm 0.3$}} \\
Medium-Expert & Hopper & $52.5$ & $\textbf{\color{thighlight}105.4}$ & $91.5$ & $\textbf{\color{thighlight}107.6}$ & $\textbf{\color{thighlight}110.0}$ & $23.7$ & $\textbf{\color{thighlight}108.7}$ & $55.1$ & $103.3$ \scriptsize{\raisebox{1pt}{$\pm 1.3$}} \\
Medium-Expert & Walker2d & $\textbf{\color{thighlight}107.5}$ & $\textbf{\color{thighlight}108.8}$ & $\textbf{\color{thighlight}109.6}$ & $\textbf{\color{thighlight}108.1}$ & $101.9$ & $44.6$ & $95.6$ & $70.2$ & $\textbf{\color{thighlight}106.9}$ \scriptsize{\raisebox{1pt}{$\pm 0.2$}} \\
\midrule
Medium & HalfCheetah & $42.6$ & $44.0$ & $\textbf{\color{thighlight}47.4}$ & $42.6$ & $\textbf{\color{thighlight}46.9}$ & $42.3$ & $42.1$ & $44.6$ & $42.8$ \scriptsize{\raisebox{1pt}{$\pm 0.3$}} \\
Medium & Hopper & $52.9$ & $58.5$ & $66.3$ & $67.6$ & $61.1$ & $28.0$ & $\textbf{\color{thighlight}95.4}$ & $48.8$ & $74.3$ \scriptsize{\raisebox{1pt}{$\pm 1.4$}} \\
Medium & Walker2d & $75.3$ & $72.5$ & $\textbf{\color{thighlight}78.3}$ & $74.0$ & $\textbf{\color{thighlight}79.0}$ & $17.8$ & $\textbf{\color{thighlight}77.8}$ & $41.0$ & $\textbf{\color{thighlight}79.6}$ \scriptsize{\raisebox{1pt}{$\pm 0.55$}} \\
\midrule
Medium-Replay & HalfCheetah & $36.6$ & $45.5$ & $44.2$ & $36.6$ & $41.9$ & $\textbf{\color{thighlight}53.1}$ & $40.2$ & $42.3$ & $37.7$ \scriptsize{\raisebox{1pt}{$\pm 0.5$}} \\
Medium-Replay & Hopper & $18.1$ & $\textbf{\color{thighlight}95.0}$ & $\textbf{\color{thighlight}94.7}$ & $82.7$ & $\textbf{\color{thighlight}91.5}$ & $67.5$ & $\textbf{\color{thighlight}93.6}$ & $12.4$ & $\textbf{\color{thighlight}93.6}$ \scriptsize{\raisebox{1pt}{$\pm 0.4$}} \\
Medium-Replay & Walker2d & $26.0$ & $77.2$ & $73.9$ & $66.6$ & $\textbf{\color{thighlight}82.6}$ & $39.0$ & $49.8$ & $9.7$ & $70.6$ \scriptsize{\raisebox{1pt}{$\pm 1.6$}} \\
\midrule
\multicolumn{2}{c}{\bf Average} & 51.9 & \textbf{\color{thighlight}77.6} & \textbf{\color{thighlight}77.0} & 74.7 & \textbf{\color{thighlight}78.9} & 42.1 & 72.9 & 47.8 & \textbf{\color{thighlight}77.5} \hspace{.6cm} \\
\bottomrule
\end{tabularx}
\vspace{-.0cm}
\caption{
}
\label{table:locomotion}
\end{table*}
To plan with guided sampling, run:
python scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs/pretrained
The --logbase flag points the experiment loaders to the folder containing the pretrained models.
You can override planning hyperparameters with flags, such as --batch_size 8, but the default
hyperparameters are a good starting point.
- Train a diffusion model with:
python scripts/train.py --dataset halfcheetah-medium-expert-v2
The default hyperparameters are listed in locomotion:diffusion.
You can override any of them with flags, eg, --n_diffusion_steps 100.
- Train a value function with:
python scripts/train_values.py --dataset halfcheetah-medium-expert-v2
See locomotion:values for the corresponding default hyperparameters.
- Plan using your newly-trained models with the same command as in the pretrained planning section, simply replacing the logbase to point to your new models:
python scripts/plan_guided.py --dataset halfcheetah-medium-expert-v2 --logbase logs
See locomotion:plans for the corresponding default hyperparameters.
Deferred f-strings. Note that some planning script arguments, such as --n_diffusion_steps or --discount,
do not actually change any logic during planning, but simply load a different model using a deferred f-string.
For example, the following flags:
---horizon 32 --n_diffusion_steps 20 --discount 0.997
--value_loadpath 'f:values/defaults_H{horizon}_T{n_diffusion_steps}_d{discount}'
will resolve to a value checkpoint path of values/defaults_H32_T20_d0.997. It is possible to
change the horizon of the diffusion model after training (see here for an example),
but not for the value function.
- Build the image:
docker build -f Dockerfile . -t diffuser
- Test the image:
docker run -it --rm --gpus all \
--mount type=bind,source=$PWD,target=/home/code \
--mount type=bind,source=$HOME/.d4rl,target=/root/.d4rl \
diffuser \
bash -c \
"export PYTHONPATH=$PYTHONPATH:/home/code && \
python /home/code/scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs"
- Build the image:
singularity build --fakeroot diffuser.sif Singularity.def
- Test the image:
singularity exec --nv --writable-tmpfs diffuser.sif \
bash -c \
"pip install -e . && \
python scripts/train.py --dataset halfcheetah-medium-expert-v2 --logbase logs"
- Tag the Docker image (built in the Docker section) and push it to Docker Hub:
export DOCKER_USERNAME=$(docker info | sed '/Username:/!d;s/.* //')
docker tag diffuser ${DOCKER_USERNAME}/diffuser:latest
docker image push ${DOCKER_USERNAME}/diffuser
-
Update
azure/config.py, either by modifying the file directly or setting the relevant environment variables. To set theAZURE_STORAGE_CONNECTIONvariable, navigate to theAccess keyssection of your storage account. ClickShow keysand copy theConnection string. -
Download
azcopy:./azure/download.sh
Launch training jobs with python azure/launch.py. The launch script takes no command-line arguments; instead, it launches a job for every combination of hyperparameters in params_to_sweep.
To rsync the results from the Azure storage container, run ./azure/sync.sh.
To mount the storage container:
- Create a blobfuse config with
./azure/make_fuse_config.sh - Run
./azure/mount.shto mount the storage container to~/azure_mount
To unmount the container, run sudo umount -f ~/azure_mount; rm -r ~/azure_mount
@inproceedings{janner2022diffuser,
title = {Planning with Diffusion for Flexible Behavior Synthesis},
author = {Michael Janner and Yilun Du and Joshua B. Tenenbaum and Sergey Levine},
booktitle = {International Conference on Machine Learning},
year = {2022},
}
The diffusion model implementation is based on Phil Wang's denoising-diffusion-pytorch repo. The organization of this repo and remote launcher is based on the trajectory-transformer repo.
