Reward Shaping
The practice of designing intermediate rewards to guide a reinforcement learning agent toward desired behavior, rather than only providing reward at the final goal state.
Why It Matters
Reward shaping accelerates RL training and prevents agents from getting stuck. It is the art of designing the right incentive structure for AI learning.
Example
For a robot learning to walk: rewarding each forward step (not just reaching the destination) so the agent gets consistent feedback on progress.
Think of it like...
Like training a puppy with treats at each step of a trick rather than only when they complete the whole sequence — more frequent feedback accelerates learning.
Related Terms
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties. The agent aims to maximize cumulative reward over time through trial and error.
Reward Model
A model trained to predict how good a response is based on human preferences. In RLHF, the reward model scores outputs to guide the language model toward responses humans prefer.
Reward Hacking
When an AI system finds unintended ways to maximize its reward signal that do not align with the designer's actual goals. The system technically optimizes the metric but violates the spirit of the objective.
Exploration vs Exploitation
The fundamental tradeoff in reinforcement learning between trying new actions (exploration) to discover potentially better strategies and using known good actions (exploitation) to maximize current reward.