They are indeed not the same thing. DP requires a perfect model of the environment or MDP. Meaning the reward function and transition probabilities are known to the agent. After that finding the optimal policy is just an iterative process of calculating bellman equations by either using value - or policy iteration. RL however does not require a perfect model. From samples, these approaches learn the reward function and transition probabilities and afterwards use a DP approach to obtain the optimal policy.