What are common reward engineering techniques?

Reward engineering focuses on designing effective reward functions to guide AI systems toward desired behaviors. Three common techniques include reward shaping, penalty design, and curriculum learning. Reward shaping involves adding intermediate rewards to help agents learn complex tasks by breaking them into smaller steps. For example, a robot learning to grasp an object might receive incremental rewards for moving closer to the target. Penalty design discourages undesirable actions by assigning negative rewards, such as deducting points when a self-driving car veers out of its lane. Curriculum learning gradually increases task difficulty, allowing agents to master basics before tackling harder challenges, like training a game AI on simplified levels before advancing to full gameplay.

Another key approach involves balancing sparse versus dense rewards. Sparse rewards, like giving a score only when a maze-solving agent reaches the exit, can lead to slow learning because feedback is infrequent. Engineers often address this by introducing denser rewards—for instance, providing small positive signals for moving toward the goal. Multi-objective reward systems combine multiple goals into a single function. A delivery drone might optimize for both speed (rewarded for shorter routes) and safety (penalized for flying near obstacles). These systems often use weighted sums or constraints to prioritize objectives, requiring careful tuning to avoid unintended trade-offs, such as sacrificing safety for speed.

Human-in-the-loop methods and inverse reinforcement learning (IRL) are also widely used. Human feedback can refine rewards by having users rate agent actions, as seen in chatbots where humans rank response quality. IRL infers reward functions from expert demonstrations, such as learning driving behavior by observing human drivers. Hybrid approaches combine automated rewards with human input—for example, using IRL to bootstrap a reward function and iteratively adjusting it based on user feedback. These techniques help align agent behavior with nuanced human goals that are hard to codify directly, ensuring the system learns both efficiently and safely.