Q-Learning Path Calculator

The Q-Learning Path Calculator is a Python-based application that utilizes Q-learning to find the shortest path for a robot on a given maze. Q-learning is a reinforcement learning algorithm that learns an optimal policy by interacting with an environment and receiving rewards for its actions.

Features

• Q-Learning Algorithm: Utilizes Q-learning to find the shortest path for a robot on a maze.
• Maze Representation: Represents the maze as a grid with obstacles and goal positions.
• Customizable Maze: Allows users to define their own maze configurations.
• Shortest Path Calculation: Finds the shortest path from the starting position to the goal position using Q-learning.

Technologies Used

• Python
• NumPy

Installation

1. Clone the repository:

   git clone https://github.com/your_username/q-learning-path-calculator.git

2. Install dependencies:

   pip install numpy

3. Run the Python script:

   python q_learning_path_calculator.py

Usage

1. Define the maze configuration in the maze.txt file. Use the following symbols:

   • S: Starting position
   • G: Goal position
   • O: Obstacle
   • .: Open path

2. Run the script and observe the robot finding the shortest path using Q-learning.

Customization

• Modify the maze.txt file to define custom maze configurations.
• Adjust the Q-learning parameters and hyperparameters in the Python script (q_learning_path_calculator.py) for different maze environments.