https://github.com/ceodaniyal/q_learning
Q-Learning Implementation for Process Optimization A reinforcement learning project that calculates the shortest route between locations using the Q-Learning algorithm. This code demonstrates how AI can optimize processes in a simulated environment with predefined states and rewards. 🚀
https://github.com/ceodaniyal/q_learning
ai-in-operations artificial-intelligence dynamic-programming machine-learning markov-decision-process pathfinding-algorithms process-optimization python q-learning reinforcement-learning reward-systems route-optimization shortest-path-algorithm state-transition-models temporal-difference-learning
Last synced: 22 days ago
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Q-Learning Implementation for Process Optimization A reinforcement learning project that calculates the shortest route between locations using the Q-Learning algorithm. This code demonstrates how AI can optimize processes in a simulated environment with predefined states and rewards. 🚀
- Host: GitHub
- URL: https://github.com/ceodaniyal/q_learning
- Owner: ceodaniyal
- Created: 2025-01-19T16:30:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-19T16:36:53.000Z (over 1 year ago)
- Last Synced: 2026-05-04T01:39:52.054Z (22 days ago)
- Topics: ai-in-operations, artificial-intelligence, dynamic-programming, machine-learning, markov-decision-process, pathfinding-algorithms, process-optimization, python, q-learning, reinforcement-learning, reward-systems, route-optimization, shortest-path-algorithm, state-transition-models, temporal-difference-learning
- Language: Python
- Homepage:
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### README for "Q-Learning Process Optimization"
---
# 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:
```bash
git clone
```
2. Navigate to the project directory:
```bash
cd q-learning-process-optimization
```
3. Run the script:
```bash
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:
```python
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`:
```python
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.