Reward Shaping
Designing intermediate reward signals to guide RL agents toward desired behavior more efficiently.
Overview
Reward shaping is the practice of designing additional intermediate reward signals (beyond the task's natural reward) to help reinforcement learning agents learn more efficiently. In many tasks, the natural reward is sparse (e.g., only awarded at game end), making it difficult for agents to discover successful strategies through random exploration.
Key Details
Well-designed reward shaping provides denser feedback that guides the agent toward productive behaviors without changing the optimal policy. However, poorly designed rewards can lead to reward hacking — agents finding unintended shortcuts. This challenge is central to AI alignment, where specifying reward functions that truly capture human intent is extremely difficult.