Reinforcement learning and evolutionary computation share the same overall goal

Reinforcement learning and evolutionary computation share the same overall goal. Both try something in an environment, evaluate the result, and use that feedback to move toward better behavior or a better solution. In this sense, they are not from different worlds; they can be seen as two approaches within the same broad family of learning methods. However, the way they improve is quite different. Evolutionary computation prepares many candidate solutions (individuals) at once, tests each of them, scores them, and keeps only the high-scoring ones to form the next generation. A key point is that it does not need to analyze in detail why a certain individual was good or which specific decisions made it succeed. It simply treats the strong individual as a parent, applies crossover and mutation, and passes on that design to the next generation. In this sense, it is a method for deciding “which individual is good.” Reinforcement learning, on the other hand, focuses on training a single agent. The agent interacts with an environment, takes actions, receives rewards, and gradually updates its own policy (what to do in each situation) based on its own experience. Here, the core idea is credit assignment: tracing which actions led to which rewards over time. This allows the same agent to improve while it is still “alive.” Evolutionary computation is strong at wide, global exploration, while reinforcement learning is strong at steadily refining one policy in detail. They are not in conflict, and they can even be combined in practice: for example, evolutionary computation can search for a good policy candidate, and reinforcement learning can then fine-tune it. In summary, their aim is similar, but the way they improve and “raise” solutions is different.