Agent environment interface
Agents are the software agents that perform actions, At, at a time, t, to move from one state, St, to another state St+1. Based on actions, agents receive a numerical reward, R, from the environment. Ultimately, RL is all about finding the optimal actions that will increase the numerical reward:
![](https://epubservercos.yuewen.com/F4348E/19470379901496006/epubprivate/OEBPS/Images/d1491480-df3b-43f1-9be5-4e3aaccd0853.png?sign=1734445630-sfIiD7inrCq7hvp42IOKV7yXwK3aE6hS-0-3b4212d64df0b02a5b46af96e0a647b6)
Let us understand the concept of RL with a maze game:
![](https://epubservercos.yuewen.com/F4348E/19470379901496006/epubprivate/OEBPS/Images/70987800-38d3-43e3-9e61-5460aac2d036.png?sign=1734445630-1QRs28TP429OGomyDvHUsjENwK7CIkPK-0-e44c1685cc4dd9f025f4a9bd49505ea3)
The objective of a maze is to reach the destination without getting stuck on the obstacles. Here's the workflow:
- The agent is the one who travels through the maze, which is our software program/ RL algorithm
- The environment is the maze
- The state is the position in a maze that the agent currently resides in
- An agent performs an action by moving from one state to another
- An agent receives a positive reward when its action doesn't get stuck on any obstacle and receives a negative reward when its action gets stuck on obstacles so it cannot reach the destination
- The goal is to clear the maze and reach the destination