How does Q-Learning behave in sparse reward environments at scale?

Q-Learning becomes inefficient in sparse reward environments due to poor exploration and weak learning signals.

When rewards are rare, most Q-updates propagate near-zero signals, making it difficult for the agent to distinguish good actions from bad ones. At scale, this issue becomes worse due to large state-action spaces where random exploration is unlikely to reach rewarding states. Techniques like intrinsic motivation, curiosity-driven exploration, and reward shaping are often required to make learning feasible.