Reinforcement learning refers to the general problem of reward maximization where the agent must try solutions for itself and get feedback of the results via some kind of evaluation &#x2F; fitness function.  (As contrasted with supervised learning, where it the agent would be given examples, and would try to learn from that).  Genetic algorithms are one specific class of optimization algorithms which accomplish this.<p>Exploration and &#x2F; vs exploitation is just a natural consequence of trying to make best sense of the unknown environment.  Different algorithms have different strategies for this, and are generally suited for different classes of problems (generally, the issue would be matching the nature of the problem with the nature of the optimization algorithm - which brings up things like the no free lunch theorem).