Reward shaping in reinforcement learning involves modifying the reward function to provide more useful feedback to the agent during the learning process. The goal is to guide the agent toward the desired behavior more effectively by offering intermediate rewards or more structured feedback.
In traditional RL, the agent only receives rewards based on the final outcome of its actions (like winning a game or reaching a goal). However, reward shaping introduces additional rewards for intermediate steps that help the agent learn faster. For example, in a maze-solving task, the agent might receive small rewards for getting closer to the goal, not just when it finishes.
While reward shaping can accelerate learning, it's important to ensure that the additional rewards don't inadvertently change the optimal policy. Careful design is needed to ensure the shaping does not lead to suboptimal behaviors that wouldn't exist in the original problem.