Reinforcement learning is not "learning from dynamic datasets as opposed from static datasets."
Reinforcement learning is learning from sequences of states/actions and a reward signal. The sequences could be totally deterministic and similar across different runs of the environment, and hence what I'm guessing you're calling "static" ("static" really has no meaning in the ML field). Or not. Go is totally deterministic, for example, but RL is still applied there.
Unsupervised learning is kind of vague, but it's supervised learning except the "desired output" (label) is generated from the input, or some desired statistic is inferred from the input. Inferring past/present or predicting future data is only a subset of the first case.
Reinforcement learning is learning from sequences of states/actions and a reward signal. The sequences could be totally deterministic and similar across different runs of the environment, and hence what I'm guessing you're calling "static" ("static" really has no meaning in the ML field). Or not. Go is totally deterministic, for example, but RL is still applied there.
Unsupervised learning is kind of vague, but it's supervised learning except the "desired output" (label) is generated from the input, or some desired statistic is inferred from the input. Inferring past/present or predicting future data is only a subset of the first case.