The notebooks in this repo illustrate optimal control and using relaxation.
This notebook provides a basic Bayesian optimisation example.
The basic mathematical formulation to these problems is available in this notebook. All of the scripts below rely on the surrogate.py GP model taken from here, which has functionality to calculate the derivatives of a GP.
This notebook implements a basic point to point control example for a moble robot in an environment with obstacles.
This notebook implements an optimal control formulation to explore an unknown space with the final state selected using a dirty heuristic.
This can produce navigation strategies that can trade-off exploration and exploitation.
This notebook implements an optimal control formulation to explore an unknown space with free final state.
