What is Reinforcement Learning?

What is Reinforcement Learning?

Reinforcement Learning, or RL for short, is machine learning that allows agents to learn from their actions and optimize their decision-making in complex, dynamic environments. The process of operant conditioning inspires this learning in psychology, where rewards or penalties are used to modify an individual’s behavior.

In reinforcement learning, an agent interacts with an environment by taking actions, receiving rewards, and observing the state of the environment. The goal of the agent is to maximize its total reward over time. The agent learns by updating its policy, which maps states to actions based on the rewards it receives. The policy is updated so that the agent selects actions that lead to higher rewards in the future.

Reinforcement learning is like a game where you make choices and get rewards or consequences based on those choices. It’s like playing a video game where you get points for making good choices and lose points for making bad choices. The more you play the game, the better you get at making the right choices to get the most points.

For example, let’s say you’re playing a candy collecting game. Every time you collect candy, you get one point. But, if you collect a gum, you lose two points. As you keep playing the game, you learn which choices to make to get the most points. This is similar to what happens in reinforcement learning. The computer program is the player and it makes decisions to get the most reward, just like you try to get the most points in the game.