The dynamic programming (DP) algorithms like policy iteration (PI) and value iteration (VI) are often presented in the context of reinforcement learning (in particular, in the book Reinforcement Learning: An Introduction by Barto and Sutton) because they are very related to reinforcement learning algorithms, like $Q$-learning - They are all based on the assumption that the environment/problem can be modelled as a Markov Decision Process (MDP).
However, PI and VI require that the transition model and reward functions of the underlying MDP are both known. They can also be referred to as planning algorithms, because they can be used to find a policy (which can be thought of as plan) given this transition model and reward function of the MDP, without having to take random actions in the environment. They just exploit the given rules of the environment.
On the other hand, RL algorithms do not usually require or use the MDP's transition model, although they still require the reward function, which is fundamental. They attempt to find a policy or value function (from which the policy can be derived) by randomly interacting with the environment.
There are several categories of RL algorithms. There are temporal-difference, Monte-Carlo, actor-critic, model-free, model-based, on-policy, off-policy, prediction, control, policy-based or value-based algorithms. These categories can overlap. For example, $Q$-learning is a temporal-difference (TD), model-free, off-policy, control and value-based algorithm: it is based on an temporal-difference (TD) update rule, it doesn't use a model of the environment (model-free), it uses a behavioural policy that is different than the policy it learns (off-policy), it is used to find a policy (control) and it attempts to approximate a value function rather than directly the policy (value-based).