locally-linear-LQG

Locally fitted dynamics for linear quadratic Gaussian controllers of a simulated 3 link robot arm. Roughly inspired by the Guided Policy Search method for trajectory optimization.

The overall process is to:

1. Generate random controls for each timestep of the trajectory.
2. Execute the controls a number of times (with some noise) to sample the dynamics around the trajectory.
3. Fit locally linear models at each timestep using the samples of each state and action.
4. Use the local models to propose new state-feedback gains and improve the LQG controllers.

The image on the left is a rendering of the robot scene, and on the right is a plot of trajectories for each iteration.