Trajectory Optimization Algorithms

This package contains trajectory optimization algorithms that are predominantly meant for continuous control taks (simulated with MuJoCo).

Installation

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.

Example Usage

See this directory for illustrative examples: trajopt/sandbox/examples.

Bibliography

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},
}