flappy-rl 0.1.1

FlappyBird game with Q-Learning AI

🏆 FlappyAlpha: Reinforcement Learning vs Human

🎮 Overview

FlappyAlpha is a reinforcement learning (RL) project inspired by AlphaGo, where an RL agent learns to play a custom Flappy Bird game environment with realistic physics (gravity, collision, etc.). The agent (FlappyAlpha) is trained using Q-Learning to maximize its score by passing obstacles and avoiding crashes. The project also features human vs. agent battles to showcase the agent's learning progress.

🎮 Features

• Custom Flappy Bird environment built with Pygame
• Q-Learning agent with reward/punishment system
• RL training for "Beginner" and "Hard" agents
• Human vs Agent battle playground with score visualization
• Performance interpretation and result plots

🎮 Tech Stack

• Game & UI: Python, Pygame
• Reinforcement Learning: Q-Learning (NumPy)
• Data Analysis & Result Visualization: NumPy, Matplotlib

🎮 Simple Demo

🎮 Project Structure

rl_flappyBird/
│
├── README.md
├── requirements.txt
├── .gitignore
│
├── src/                        # Core game environment
│   └── flappy_bird.py
│
├── train/                      # Training scripts
│   ├── train_qLearningBEGINNER.py
│   └── train_qLearningHARD.py
│
├── models/                     # Trained models
│   ├── best_q_table_beginner.npy
│   └── best_q_table_hard.npy
│
├── results/                    # Result plots and analysis
│   ├── beginner_mode_scores.png
│   ├── hard_mode_scores.png
│   ├── user_vs_agent_scores.png
│   └── user_vs_agent_scoresHARD.png
│
├── playground/                 # Human vs agent
│   ├── playground.py
│   └── playgroundHARD.py
│
└── assets/                     # Assets

🎮 How to Use

U can use virtual environment (venv), but since the dependencies are just common and less, so depend on yourself.

1. Install Dependencies

pip install -r requirements.txt

2. Train the RL Agent

Note: Pre-trained Q-tables are provided in the models/ folder. You can retrain if you wish.

• Train Beginner mode agent:

  python train/train_qLearningBEGINNER.py
  

• Train Hard mode agent:

  python train/train_qLearningHARD.py
  

3. Human vs. Agent Battle

• Play against Beginner agent:

  python playground/playground.py
  

• Play against Hard agent:

  python playground/playgroundHARD.py
  

4. View Results

• Training and battle results are auto saved as .png plots in the results/ folder.

🎮 Highlights & Insights

• The RL agent receives positive rewards for passing pipes/blocks and negative penalties for crashing.
• As training episodes increase, the agent's performance improves dramatically:
  • After 2500 episodes (beginner mode): Best score 17.
  • After 3000 episodes (hard mode): Best score 75 (441% increase).
  • After 4000 episodes: Best score 3693 (near-perfect play).

  U can modify the training parameter, ex. EPISODES = 4000 if u want.

• Human players can easily beat the Beginner agent (2500 episodes), but the Hard agent (3000 episodes) is a tough opponent, often outperforming humans in average score, and I'm sure for > 4000 episodes training, human can't beat RL agent as it plays near perfect.

🎮 Acknowledgements

Inspired by AlphaGo and the power of reinforcement learning.

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💎 Feel free to fork, do experiment, and challenge FlappyAlpha.