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---
license: mit
language:
- en
---
# README for Flappy-AI with Reinforcement Learning
Model for the repository [flappy-ai](https://github.com/amc-madalin/flappy-ai), an project that utilizes reinforcement learning, to train an AI agent to master the game of Flappy Bird. This model is the 20th episode for the following hyperparameters:
# File: config.yml
```
game_screen:
width: 800
height: 600
game_mechanics:
gravity: 5
flap_strength: 20
pipe_speed: 2
pipe_width: 100
pipe_gap: 200
character_size: 50
collision_penalty: -1 # Penalty for hitting a pipe
pass_reward: 1 # Reward for successfully passing a pipe
height_penalty: -0.1 # Penalty for flying too high or too low
initial_positions:
character_x: 350
character_y: 250
pipe_x: 800
pipe_height_min: 100
pipe_height_max: 400
ai_config:
epsilon: 0.3
epsilon_min: 0.01
epsilon_decay: 0.98
gamma: 0.1
learning_rate: 0.0001
batch_size: 32
buffer_size: 5000
num_episodes: 500
other_settings:
random_seed: 42
model:
save_path: ./experiments/
load_path: ~
```
## Project Structure
- `ai/`: This directory houses the core AI components, including the Q-network for decision making, the replay buffer for learning from past actions, and the training logic essential for the agent's learning process.
- `assets/`: Here, you'll find all the graphical and audio assets utilized in the project, enriching the game's visual and auditory experience.
- `configs/`: Contains various configuration files that allow customization and fine-tuning of the AI agent and the game environment.
- `environment.yml`: A comprehensive list of dependencies required for the project, ensuring consistent setup across different environments.
- `experiments/`: This section includes experimental code and data, showcasing the trials and tests conducted during the development process.
- `game/`: Contains the game's logic and rendering code, the heart of the Flappy Bird game that the AI interacts with.
- `model/`: Here lies the trained AI model, a testament to the learning and adaptation achieved through reinforcement learning.
- `runs/`: Stores outputs from various training runs, providing insights into the AI's performance and learning progression.
- `utils/`: A collection of utility scripts and modules that support various functionalities across the project.
- `train.py`: The primary script used to kickstart the AI agent's training process.
- `test_game.py`: A testing script to run the game independently of the AI agent, useful for debugging and game playthroughs.
## Installation
To set up this project on your local machine, follow these steps:
1. Clone the repository:
```sh
git clone https://github.com/amc-madalin/flappy-ai.git
```
2. Navigate to the project directory:
```sh
cd flappy-ai
```
3. Install the required dependencies:
```sh
conda env create -f environment.yml
```
## Usage
Once the installation is complete, you can start exploring Flappy-AI.
1. Activate the project environment:
```sh
conda activate flappy-ai
```
2. To begin training the AI agent, execute:
```sh
python train.py
```
3. To test the game mechanics without the AI agent, run:
```sh
python test_game.py
```
## License
This project is released under the MIT License, promoting open-source collaboration and sharing.
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