Robot Learning

Sparse Reward

A sparse reward is a reinforcement learning reward signal that is nonzero only at rare events, typically task completion, giving the agent no gradient of progress during the vast majority of exploration. Sparse rewards are easy to specify correctly but make credit assignment and exploration hard, especially over long horizons. Standard remedies include reward shaping, hindsight experience replay, curriculum learning, and seeding with demonstrations.

Why it matters for physical AI

Task success is trivial to define but brutal to learn from; managing the specification-versus-learnability trade-off is a recurring theme in training manipulation policies with RL.