This package contains trajectory optimization algorithms that are predominantly meant for continuous control taks (simulated with MuJoCo).
The main package dependencies are python>=3.5, gym, mujoco_py, and numpy. The algorithms assume an environment abstraction similar to mj_envs, which builds on top of the gym abstraction.
After mujoco_py has been installed, the package can be used by either adding to path as:
export PYTHONPATH=<path/to/trajopt>$PYTHONPATH
or through the pip install module
$ cd trajopt
$ pip install -e .
The tricky part of the installation is likely to be mujoco_py. Please see instructions and known issues for help.
See this directory for illustrative examples: trajopt/sandbox/examples.
If you find the package useful, please cite the following paper.
@INPROCEEDINGS{Lowrey-ICLR-19,
AUTHOR = {Kendall Lowrey AND Aravind Rajeswaran AND Sham Kakade AND
Emanuel Todorov AND Igor Mordatch},
TITLE = "{Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control}",
BOOKTITLE = {ICLR},
YEAR = {2019},
}