Robot Learning

Reward Function

A reward function is the scalar signal that defines a reinforcement learning objective, mapping each state and action, or resulting transition, to a numeric reward whose cumulative discounted sum the agent maximizes. It is the formal specification of the task within a Markov decision process. Sparse rewards, given only at success, are easy to specify but hard to explore under, while dense rewards guide learning at the risk of biasing the solution.

Why it matters for physical AI

Everything an RL-trained robot does is downstream of its reward function, so the gap between the reward as written and the behavior actually desired is a first-order safety and reliability concern.