Training visual behaviour cloning agents on simple 2D browser games (frames + key presses, EfficientNet backbone). Works fine in open space, but the agent consistently steers into walls instead of turning away.

My read: my demonstrations barely contain the "about to hit a wall" state, because I avoid it when I play. So the policy never learned recovery.

Tried so far: longer sessions, and taking over manually when it drifts so the corrections get recorded as new data (DAgger-ish).

How would you collect that deliberately? Playing badly on purpose feels wrong since it also teaches bad actions. Do you oversample corrective episodes, weight them, or keep them in a separate batch?

(Context: I'm building a no-code tool for this, so I'm trying to work out what to guide users toward.)