Definition
Plain language
Adding extra small rewards along the way to guide a learning agent toward a goal that's otherwise rarely reached.
As stated in the literature
Augmenting a sparse reward with auxiliary intermediate signals to ease credit assignment; potential-based shaping (Ng et al.) provably preserves the optimal policy while accelerating learning.
Also called: shaping reward, potential-based shaping
Why it matters: It makes learning feasible on tasks where success is rare, and a careful form of it can speed learning without changing the best final behavior.
For example, instead of rewarding a robot only when it reaches the exit, you give it small rewards for getting closer to guide it along the way.
Heard on the show
“The shaping reward says — relative to what successful agents looked like at this point, you just moved closer.”Episode 008 — Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps