This repo is the codebase for the SCA 2020 paper with the title above. The full talk is available here.

This library should run on Linux, Mac, or Windows.

# TODO: Create and activate virtual env

# Download the repo as well as the submodules
git clone https://github.com/belinghy/SteppingStone --recurse-submodules

cd SteppingStone
pip install -r requirements

The enjoy.py script can be used to run pretrained policies and render the results. Hit r in the PyBullet window to reset.

# Run Mike controller
python -m playground.enjoy --env mocca_envs:MikeStepperEnv-v0 \
    --net playground/models/mocca_envs:MikeStepperEnv-v0_latest.pt

# Run Walker3D controller
python -m playground.enjoy --env mocca_envs:Walker3DStepperEnv-v0 \
    --net playground/models/mocca_envs:Walker3DStepperEnv-v0_latest.pt

To start a new training experiment named test_experiment for the MikeStepper environment you can run:

./scripts/local_run_playground_train.sh test_experiment \
    env_name='mocca_envs:MikeStepperEnv-v0'

# Train with curriculum (see plaground/train.py for arguments)
./scripts/local_run_playground_train.sh  curriculum_experiment \
    env_name='mocca_envs:MikeStepperEnv-v0' use_curriculum=True

This command will create a new experiment directory inside the runs directory that contains the following files:

• pid: the process ID of the task running the training algorithm
• progress.csv: a CSV file containing the data about the the training progress
• slurm.out: the output of the process. You can use tail -f to view the contents
• configs.json: a JSON file containing all the hyper-parameter values used in this run
• run.json: extra useful stuff about the run including the host information and the git commit ID (only works if GitPython is installed)
• models: a directory containing the saved models

If you use Compute Canada, we also have scripts like cedar_run_playground_train.sh to create a batch job. These scripts use the same argument sctructure but also allow you to run the same task with multiple replicates using the num_replicates variable.

The plot_from_csv.py script can be helpful for plotting the learning curves:

python -m playground.plot_from_csv --load_paths runs/*/*/ \
    --columns mean_rew max_rew  --smooth 2

# group results based on the name
python -m playground.plot_from_csv --load_paths runs/*/*/  \
    --columns mean_rew max_rew  --name_regex ".*__([^_\/])*" --group 1

• The load_paths argument specifies which directories the script should look.
• It opens the progress.csv file and plots the columns as the y-axis and uses the row for the x-axis (defaults to total_num_steps).
• You can also provide a name_regex to make the figure legends simpler and more readable, e.g. --name_regex 'mike-(.*)\/'.
• group can be used to aggregate the results of multiple runs of the same experiment into one. name_regex is used to specify the groups.

Please cite the following paper if you find our work useful.

@inproceedings{2020-SCA-ALLSTEPS,
  title={ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills}
  author={Xie, Zhaoming and Ling, Hung Yu and Kim, Nam Hee and van de Panne, Michiel},
  booktitle = {Proc. ACM SIGGRAPH / Eurographics Symposium on Computer Animation},
  year={2020}
}

The preprint is also available on ArXiv.