Welcome to repr-control’s documentation!
repr-control is a toolbox to solve nonlinear stochastic control via representation learning. User can simply input the dynamics, rewards, initial distributions (See Running Experiments for sample code) of the nonlinear control problem and get the optimal controller parametrized by a neural network.
The optimal controller is trained via Spectral Dynamics Embedding Control (SDEC) algorithm based on representation learning and reinforcement learning. For those interested in the details of SDEC algorithm, please check our papers.
Check out the Installation instructions, and the Usage section for further information.
Note
This project is under active development.