PYTHON · NUMPY

Linear Behavior Cloning

You fit a single linear map from observation to action in closed form. How does it do against predicting the mean action?

Fit the simplest possible policy, one linear map from observation to action, in closed form, and measure it on held-out episodes.

01Challenge

Solve a least-squares behavior-cloning policy and confirm it beats the mean baseline on the held-out episodes.

Predict firstYou fit a single linear map from observation to action in closed form. How does it do against predicting the mean action?

02Model

Behavior cloning is supervised learning on a robot: fit a function from observation to the action the expert took. Start with the simplest function there is, a straight line, solved in one shot by least squares. It will not be perfect, the expert's law curves, but a linear policy learned from data already leaves the hand-coded rule far behind. This is your floor; everything after this earns its keep by beating it.

03Guided practice

1. Fit on train only. Compute the normalization stats on the training frames, then z-score everything with them.

2. Least squares. Add a bias column of ones so the line can shift, then solve A · Wb ≈ actions with np.linalg.lstsq. That is behavior cloning in closed form.

3. Score it on held-out data. Report the mean squared error on the held-out episodes, and the fraction of the action variance it explains.

04Feedback

PASS WHEN PASS when the linear policy's held-out MSE is well under the mean baseline (it explains most of the action variance).

If your run fails, check:

Read the bench’s printed result, it passes when the linear policy's held-out MSE is well under the mean baseline (it explains most of the action variance). Run the cells top to bottom; if one errors, the FAIL line names the step to fix.
The least-squares solve should clearly beat predict-the-mean.

05Retrieve & space

What does behavior cloning fit? (a function from observation to the expert's action).
How is a linear policy solved? (least squares, in closed form).
Why add a bias column of ones? (so the linear map can shift, not just rotate).

06Mastery & project

You can fit and correctly score a linear behavior-cloning policy, the floor every richer policy must beat.

A reflex worth keeping: always fit the simplest baseline first, so you know what 'better' has to mean.

← The Dataset Is the Program A Neural Policy, Trained by Gradient Descent →

PYTHON · NUMPY

Linear Behavior Cloning

Fit a linear behavior-cloning policy with least squares and evaluate it on held-out episodes.

You read

the arrays and values already in scope

You change

the code you write in each cell

Fixed

the dataset and the checks that grade you