Reward shaping in reinforcement learning (RL) is a technique used to improve an agent’s learning process by modifying the reward signal it receives from the environment. In RL, agents learn by maximizing cumulative rewards, but sparse or poorly structured rewards (e.g., receiving a reward only upon completing a task) can make learning slow or ineffective. Reward shaping addresses this by adding supplemental rewards that guide the agent toward desired behaviors. For example, in a maze navigation task, the default reward might be +1 for reaching the goal and 0 otherwise. With reward shaping, the agent could receive small positive rewards for moving closer to the goal, even if it hasn’t reached it yet. These intermediate rewards help the agent learn faster by providing clearer feedback during exploration.
A common approach to reward shaping is potential-based shaping, which ensures the supplemental rewards don’t alter the optimal policy—the best possible strategy for the agent. This method uses a potential function that assigns a value to each state based on its desirability. The supplemental reward is then calculated as the difference in potential between the current state and the next state. For instance, in the grid world example, the potential function could measure the Manhattan distance to the goal. If the agent moves from a state 5 units away to 4 units away, the shaping reward would be +1. This approach maintains the original goal (reaching the endpoint) while encouraging progress. Developers often implement this by defining domain-specific heuristics, such as rewarding a robot for facing the correct direction or penalizing a game character for stepping into hazardous areas temporarily.
While reward shaping can accelerate learning, it requires careful design. Poorly chosen supplemental rewards might lead the agent to exploit the shaped rewards instead of solving the actual task. For example, if an agent in a survival game is rewarded for collecting health packs, it might prioritize hoarding them instead of defeating enemies. Over-shaping can also make the agent overly dependent on the designer’s assumptions, reducing its ability to adapt to new scenarios. To mitigate this, developers should test shaped rewards in simplified environments first and validate that the agent’s behavior aligns with the intended goal. When applied correctly, reward shaping balances guidance with flexibility, making it a practical tool for complex RL problems like robotics control or game AI.