Welcome to our Hackathon project repository, organized by Lablab.AI! In this challenge, we were tasked with creating various types of agents that can carry out several tasks. Our team has developed a multi-agent system that can efficiently perform tasks in a simulated environment. This project showcases our innovative approach to problem-solving and demonstrates the power of collaboration.

Before diving into the project, make sure you have the following software installed on your machine:

1. Python 3.7 or higher
2. Git
3. Virtualenv (optional, but recommended)

Additionally, you'll need to install the required Python packages. You can find the list of dependencies in the requirements.txt file.

To set up the project on your local machine, follow these steps:

1. Clone the repository:

git clone https://github.com/your-username/hackathon-agents.git

2. (Optional) Create a virtual environment and activate it:

virtualenv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

pip install -r requirements.txt

4. Run the main script to see the agents in action:

python main.py

The project is organized as follows:

hackathon-agents/
│
├── agents/          # Agent classes and related utilities
│   ├── __init__.py
│   ├── base_agent.py
│   ├── agent1.py
│   ├── agent2.py
│   └── ...
│
├── environment/     # Environment simulation and related utilities
│   ├── __init__.py
│   ├── base_env.py
│   ├── env1.py
│   ├── env2.py
│   └── ...
│
├── data/            # Data files used in the project
│   ├── dataset1.csv
│   ├── dataset2.csv
│   └── ...
│
├── main.py          # Main script to run the project
├── requirements.txt # List of required Python packages
└── README.md        # This file

The data used in this project consists of several CSV files, which can be found in the data/ directory. These files contain information about the tasks, agents, and environment. The data is used to train and evaluate the performance of the agents in the simulated environment.

Throughout the development of this project, we referred to various resources to help us better understand the problem and implement our solution. Some of the key references include:

1. Multi-Agent Systems: A Survey
2. Reinforcement Learning for Multi-Agent Systems
3. Lablab.AI Hackathon Guidelines

Our team has successfully developed a multi-agent system that can perform various tasks in a simulated environment. We believe that our solution demonstrates the potential of multi-agent systems in solving complex problems and highlights the importance of collaboration in the field of artificial intelligence.

We would like to thank all the team members for their hard work and dedication throughout the Hackathon. We would also like to express our gratitude to Lablab.AI for organizing this event and providing us with the opportunity to showcase our skills and learn from our peers.