Hands-On Reinforcement Learning with Python

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, ...