Teaching an Agent to Escape a Maze Using Reinforcement Learning (With Animation!)

Reinforcement Learning (RL) enables machines to learn optimal behavior through interaction with an environment rather than explicit programming. Instead of relying on labeled datasets, the agent improves by receiving feedback in the form of rewards and penalties.

In this project, I developed an animated maze environment where an autonomous agent learns to navigate from a starting point to a target location. The project demonstrates how reinforcement learning concepts can be applied to decision-making problems and how visualization can improve interpretability of learning behavior.

This article presents the design approach, learning algorithm, training results, and key insights from the implementation.

Project Objectives

The main objectives of this project were:

• Design a grid-based maze environment
• Train an agent to identify the shortest path to the goal
• Visualize the agent’s behavior using animation
• Evaluate learning performance across training episodes

The focus was not only on achieving task completion but also on ensuring learning stability and efficiency.