Building stochastic environments for training RL agents
To train RL agents for the real world, we need learning environments that are stochastic, since real-world problems are stochastic in nature. This recipe will walk you through the steps for building a Maze learning environment to train RL agents. The Maze is a simple, stochastic environment where the world is represented as a grid. Each location on the grid can be referred to as a cell. The goal of an agent in this environment is to find its way to the goal state. Consider the maze shown in the following diagram, where the black cells represent walls:
Figure 2.1 – The Maze environment
The agent's location is initialized to be at the top-left cell in the Maze. The agent needs to find its way around the grid to reach the goal located at the top-right cell in the Maze, collecting a maximum number of coins along the way while avoiding walls. The location of the goal, coins, walls, and the agent...
