🦖 Chrome Dino Reinforcement Learning Agent

This project showcases a Deep Q-Network (DQN) based reinforcement learning agent trained to play the Chrome Dino game. Built using PyGame and Gym style wrapper, the agent learns to survive and jump over obstacles effectively, mimicking human play through observation-based state input.

Demo Link:

https://www.linkedin.com/posts/tanmay-singh-429ab81b9_ai-reinforcementlearning-python-activity-7341131692671234049-KaB8?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAADLi-2EBDMofotInT29A0nDfI1120hvUmCE

🎮 Features

• Custom Game Environment: Recreated Chrome Dino game using PyGame
• Gym-Compatible: Environment is wrapped using Gym style wrapper for easy training/testing
• DQN Agent: Deep Q-Learning with experience replay and target network
• Frame Skipping: Improves training efficiency and simulates human-like reaction
• Metrics & Logging: Training reward graphs and test performance included

🚀 Installation

1. Clone the repo:

   git clone https://github.com/Tanmay0766/Ai-Dino-Agent-using-DQN.git
   cd Ai-Dino-Agent-using-DQN
   

2. Create the Conda Environment:

   Create a new environment using the provided environment.yml file:

   conda env create -f environment.yml

   Activate the Environment: conda activate your-env-name

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run training:

   python train.py
   

5. Test the trained agent:

   python test.py
   

🧠 State Description

The agent receives the following state vector:

1. Dino's vertical position
2. Vertical velocity
3. Distance to next cactus
4. Cactus height
5. Cactus width

📂 File Structure

1. game.py_# Original dino game
2. dino_env.py_# Pygame environment logic
3. gym_dino_env.py_# Gym wrapper
4. agent.py_# DQN agent
5. train.py_# Training script
6. test1.py_# Testing script
7. test2.py_# Test metrices and graph
8. best_dqn_dino.pth_# Saved model (trained)
9. README.md

📜 License

This project is licensed under the MIT License.