Reward hacking occurs when an agent exploits its specified reward function to behave in undesirable or unsafe ways. Aside from better alignment between the specified reward function and the system designer's intentions, a more feasible proposal to prevent reward hacking is to regularize the learned policy to some safe baseline. Current research suggests that regularizing the learned policy's action distributions to be more similar to those of a safe policy can mitigate reward hacking; however, this approach fails to take into account the disproportionate impact that some actions have on the agent’s state. Instead, we propose a method of regularization based on occupancy measures, which capture the proportion of time each policy is in a particular state-action pair during trajectories. We show theoretically that occupancy-based regularization avoids many drawbacks of action distribution-based regularization, and we introduce an algorithm called ORPO to practically implement our technique. We then empirically demonstrate that occupancy measure-based regularization is superior in both a simple gridworld and a more complex autonomous vehicle control environment.