DQN Framework for Web-Based Game Automation
Overview
This project develops a customizable Deep Q-Network (DQN) framework for automating web-based games using reinforcement learning. The framework is built with PyTorch and uses Selenium for real-time game data extraction for web games. The primary application is the Chrome Dino game, serving as a baseline model. The framework is tested with multiple open ai gym environemtns to ensure compatibility and extendability.
Installation
- Clone the repository:
git clone https://github.com/prabhath-r/DQN_Framework_RL-Game - Install dependencies:
pip install -r requirements.txt - Run the main script:
python main.py --game_env [game_name]
Code Structure
dqn_agent.py: Implements the DQN agent, capable of decision-making and learning.environment.py: Manages the interaction between the DQN agent and the game.model.py: Defines the neural network architecture for the DQN agent.config.py: Contains hyperparameter settings for the DQN agent.train_utils.py: Contains helper functions to dynamically save and load the previous checkpointsmain.py: The main executable script for training the agent.
Usage
- Training the Agent: Execute
main.pywith the desired game environment. - Modifying Hyperparameters: Adjust settings in
config.pyfor custom training scenarios. - Environment Adaptation: Use
environment.pyto interface with new or existing game environments.
Key Features
- Customizable DQN Agent: Implements state-of-the-art decision-making and learning capabilities.
- Dynamic Environment Management: Seamlessly integrates with various web-based game environments.
- Flexible Neural Network Architecture: Tailors to specific game dynamics, enhancing learning efficiency.
- Comprehensive Training Suite: Includes tools and scripts for effective training and performance evaluation.