Solving the Gridworld Problem Using Reinforcement Learning in Python
Reinforcement Learning (RL) is an exciting and powerful paradigm that allows agents to learn optimal behaviors through trial and error. In this post, we will explore how to solve the Gridworld problem using Q-learning, one of the foundational RL algorithms, and implement it using Python and NumPy.
Another post on the use of RL is Exploring the Multi-Armed Bandit Problem with Python: A Simple Reinforcement Learning Example. The code for this post can be found at this GitHub page.
What is the Gridworld Problem?
The Gridworld problem involves an agent navigating a grid to reach a goal while avoiding obstacles. The environment is represented as a grid where each cell can either be empty, contain an obstacle, or be the goal. The agent starts at a designated point (usually the top-left corner) and must find the most efficient path to reach the goal. The challenge is that the agent doesn’t know the grid layout beforehand and must learn it through exploration.
The Environment
We define a 5x5 grid with the following properties:
- The agent starts at the top-left corner
(0, 0). - The goal is at the bottom-right corner
(4, 4)with a high positive reward of+10.
