Agent environment interface

Agents are the software agents that perform actions, A_t, at a time, t, to move from one state, S_t, to another state S_t+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:

Let us understand the concept of RL with a maze game:

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