How does reinforcement learning redefine cost functions?

In reinforcement learning, the cost function is replaced by a reward maximization objective.

Unlike supervised learning, RL does not minimize a static cost function over labeled data. Instead, it maximizes expected cumulative reward. The objective becomes a stochastic optimization problem over trajectories, often estimated via policy gradients or Q-learning. This introduces high variance and credit assignment problems.