markov-decision-process

I've seen such words as: A policy defines the learning agent's way of behaving at a given time. Roughly speaking, a policy is a mapping from perceived states of the environment to actions to be taken when in those states. But still didn't fully understand. What exactly is a policy in reinforcement learning? The definition is correct, though not instantly obvious if you see it for the first time. Let me put it this way: a policy is an agent's strategy . For example, imagine a world where a robot moves across the room and the task is to get to the target point (x, y), where it gets a reward.