BlastPursuit

Overview

BlastPursuit is a Python-based project designed for training and evaluating reinforcement learning agents in a gaming scenario. It provides tools for training agents and evaluating their performance using a predefined environment. The project includes a single agent implementation for users to experiment with.

Installation

To use BlastPursuit, follow these steps:

• Clone the repository:

cd repo_folder
git clone https://github.com/Lauqz/BlastPursuitMARL.git

• Install the required packages using pip:

pip install -r requirements.txt

Usage

• Training: Launch the training process using the following command:

python training.py

• Evaluation: Evaluate the trained agent by running the evaluation script:

python evaluate.py

Additional Notes

• Convergence Monitoring: Monitor the training convergence using TensorBoard. Run the following command in the project directory:

tensorboard --logdir tensorboard

• GUI Modification: To view the GUI during training, modify the utils.py file. Change the value of TRAINING from True to False.

Authors and acknowledgment

Authors: Guido Laudenzi and Sami Osman. Thanks to the contributors of the open-source libraries used in this project.

License

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