https://github.com/armanx200/rl-requestmaximizer

🚀 Async Python agent using Reinforcement Learning 🧠 to optimize HTTP requests 🌐. Dynamically adjusts delay & retries 🔄 for resilience in unstable networks. Ideal for API-heavy projects needing efficient request handling! ⚙️
https://github.com/armanx200/rl-requestmaximizer

agent-based aiohttp api arman-kianian artificial-intelligence async asyncio automation data-science free http machine-learning open-source optimization performance python reinforcement-learning scalability scraping web3

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🚀 Async Python agent using Reinforcement Learning 🧠 to optimize HTTP requests 🌐. Dynamically adjusts delay & retries 🔄 for resilience in unstable networks. Ideal for API-heavy projects needing efficient request handling! ⚙️

• Host: GitHub
• URL: https://github.com/armanx200/rl-requestmaximizer
• Owner: Armanx200
• License: mit
• Created: 2024-10-29T12:28:39.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-10-29T12:33:32.000Z (2 months ago)
• Last Synced: 2024-10-29T14:54:45.072Z (2 months ago)
• Topics: agent-based, aiohttp, api, arman-kianian, artificial-intelligence, async, asyncio, automation, data-science, free, http, machine-learning, open-source, optimization, performance, python, reinforcement-learning, scalability, scraping, web3
• Language: Python
• Homepage: https://Armanx200.Github.io
• Size: 18.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 🚀 **RL-RequestMaximizer** 🌐

**An async Python agent that optimizes HTTP requests using Reinforcement Learning (RL).**  

Automatically adjusts request delays and retry counts to improve resilience and boost request success rates in network-constrained or unstable environments.

## 📖 **About the Project**

RL-RequestMaximizer is designed to help API-heavy applications make more reliable HTTP requests by learning the best settings for delay and retries. This async agent uses **Reinforcement Learning** to self-optimize in real time, reducing failures and making network operations smoother and more efficient.

## ✨ **Features**

- **Adaptive Delay & Retry Logic**: Dynamically adjusts based on reward feedback.

- **Reinforcement Learning** 🧠: Self-tunes delay and retries based on request success.

- **Fully Asynchronous** 🔄: Built with `aiohttp` and `asyncio` for high-performance networking.

- **Persistence** 💾: Save and load trained models to retain learning over sessions.

---

## ⚙️ **Installation**

First, clone the repository and navigate to the project directory:

```bash

git clone https://github.com/Armanx200/RL-RequestMaximizer.git

cd RL-RequestMaximizer

```

Then, install the required packages:

```bash

pip install -r requirements.txt

```

---

## 🚀 **Usage**

1. **Train the Agent**: Run `app.py` to start training the RL agent on the request environment.

   ```bash

   python app.py

   ```

   Training progress and agent status will be logged to the console as shown below:

   ```

   Epoch 1/200 | Delay: 0.80 | Max Retries: 3 | Success Count: 800 | Reward: 0.80

   ```

2. **Load a Trained Agent**: To load a previously saved agent for reuse:

   ```python

   agent = RLAgent.load("trained_agent.pkl")

   ```

3. **Customize Parameters**: You can modify the number of requests or training epochs directly in `app.py` as needed.

---

## 📜 **License**

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more information.

---

Happy optimizing! 🚀