README for "Q-Learning Process Optimization"

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Q-Learning Implementation for Process Optimization

This project demonstrates a Q-Learning implementation to optimize routes between locations in a predefined environment. It uses the principles of reinforcement learning to determine the shortest path between locations while considering rewards for each state transition.

Features

• Environment Definition: States, actions, and reward matrices define the system's environment.
• Reinforcement Learning: Implements the Q-Learning algorithm to learn and optimize routes.
• Shortest Route Calculation: Calculates the shortest route between a starting location, intermediary location, and ending location.

Getting Started

Prerequisites

• Python 3.x
• Numpy library (pip install numpy)

Installation

1. Clone the repository:

   git clone <repository_url>

2. Navigate to the project directory:

   cd q-learning-process-optimization

3. Run the script:

   python q_learning_optimization.py

Usage

1. Define your starting, intermediary, and ending locations.
2. Use the best_route function to calculate the optimal route:

   print(best_route('E', 'K', 'G'))

3. Modify the reward matrix (R) to represent different environments as needed.

Example

To calculate the optimal route from location E to K to G:

print(best_route('E', 'K', 'G'))

Output:

Route:
['E', 'I', 'J', 'K', 'G']

How It Works

1. Q-Learning Algorithm:
   • Randomly explores the environment.
   • Updates the Q-Table using the Temporal Difference (TD) formula.
2. Route Calculation:
   • Starts from the initial state.
   • Iteratively selects the next state with the highest Q-value until reaching the destination.

Customization

• Adjust gamma (discount factor) and alpha (learning rate) to fine-tune the learning process.
• Modify the reward matrix (R) to represent different environments.

License

This project is licensed under the MIT License. See the LICENSE file for details.