What do "agent", "environment", "reward", and "action" refer to in Reinforcement Learning?

Hello! I'm happy to share my understanding of these fundamental concepts in reinforcement learning, hoping it helps you get started.

Imagine you're training a puppy (let's call him "Wangcai") to learn a new trick, like "sit." This process is very similar to reinforcement learning.

Agent - The Decision-Making "Learner"

In this example, "Wangcai" is the Agent.

The Agent is the protagonist of our story, the entity that needs to learn and make decisions. It could be a robot, a character in a game, or a chess program. Its task is to observe its surroundings and decide what to do next.

Agent

Environment - The Agent's "World"

The living room where you and Wangcai are is the Environment.

The Environment is where the Agent lives and interacts. For Wangcai, the environment includes the floor, furniture, you (the trainer), and your commands. The environment changes based on the Agent's actions; for example, if Wangcai runs from the carpet to the sofa, that's a change in the environment's state.

Action - The Agent's "Choices"

Everything Wangcai can do, such as "sit," "lie down," "bark," "wag its tail," etc., are Actions.

Actions are a set of operations that the Agent can perform in a given situation. At each point in time, the Agent needs to choose one of these possible actions to execute.

Reward - The "Feedback" on Action Quality

The treats or verbal praise you give Wangcai are Rewards.

This is the most crucial part of the entire learning process. After the Agent performs an action, the Environment provides feedback, which is the "Reward."

Positive Reward: If Wangcai obeys your "sit" command and actually sits down, you'll give him a treat. This positive feedback tells him: "What you just did was great!"
Negative Reward (or Punishment): If he tries to chew on the sofa, you might scold him. This is negative feedback, telling him: "That action was bad; don't do it again."
No Reward: If he just stands there doing nothing, you might not give him anything.

The Agent's goal is very simple: to figure out a sequence of actions that maximizes the total reward (treats) it receives throughout the process.

In Summary:

Agent: The learner and decision-maker (Wangcai).
Environment: The world the Agent is in (the living room).
Action: The operations the Agent can perform (sit, roll over, etc.).
Reward: Immediate feedback on the quality of an action (treats or criticism).

The entire reinforcement learning process is as follows: The Agent (Wangcai), within the Environment (living room), continuously tries various Actions (like sitting), and adjusts its behavior strategy based on the Rewards (treats) it receives, ultimately learning how to make the best choices in various situations to obtain the maximum cumulative reward.