Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing actions and receiving rewards.
Think of it like learning to play a video game. When you start, you might not know what to do, so you try different things. If you do something that gets you points or helps you progress, that’s good, so you try to do it again in the future. If you do something that causes you to lose points or makes the game harder, that’s bad, so you try to avoid doing it again. That’s the basic idea of reinforcement learning.
In reinforcement learning, there’s an agent (like you playing the video game), which can take actions (like moving a character or pressing a button), and there’s an environment (the game itself). When the agent takes an action, the environment gives the agent a reward (like points) and provides a new state (like the next level of the game). The goal of the agent is to learn a policy, which is a strategy for choosing actions that will maximize the total reward over time.
Reinforcement learning is used in many areas of artificial intelligence, such as robotics, game playing, and autonomous driving, where the AI needs to learn how to make a series of decisions to achieve a goal.
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