https://github.com/dhyanesh18/flappbird-rl
PPO agent and A2C agents for Flappybird. Includes scripts, training code, and evaluation tools.
https://github.com/dhyanesh18/flappbird-rl
a2c flappybird opencv ppo pygame-learning-environment reinforcement-learning stablebaselines3
Last synced: 28 days ago
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PPO agent and A2C agents for Flappybird. Includes scripts, training code, and evaluation tools.
- Host: GitHub
- URL: https://github.com/dhyanesh18/flappbird-rl
- Owner: Dhyanesh18
- Created: 2025-07-13T14:05:16.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-15T18:52:15.000Z (11 months ago)
- Last Synced: 2025-07-16T17:05:10.287Z (11 months ago)
- Topics: a2c, flappybird, opencv, ppo, pygame-learning-environment, reinforcement-learning, stablebaselines3
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# FlappyBirdRL
A Reinforcement Learning (RL) agent that learns to play Flappy Bird using **Stable Baselines3** and a custom **OpenAI Gym** environment.
This project demonstrates **deep RL** for an arcade-style game, with PPO/A2C and entropy annealing for improved exploration.
## Test clip
## Training graph (PPO)
---
## My setup and work
- The agents were trained on visual mode of flappy bird rather than numerical values of velocity, position etc.., so that its same as how humans percive the game.
- 4 frames are stacked while passing through the CNN so that the agent understands the temporal information from the environment.
- Entropy coefficient annealing is done so that the model stops exploring and starts exploiting at the later half of the training.
- The config in the code files are what were used to get the best results.
- Sadly, hyperparameter tuning wasn't possible and mostly intuition based tuning was done as the training of an agent for 10M timesteps took 19.2 Hrs.
## Project Highlights
- **Algorithms:** PPO & A2C from Stable Baselines3
- **Custom Gym Env:** Pixel-based Flappy Bird with frame skipping & stacking
- **Entropy Annealing:** Controls exploration dynamically
- **TensorBoard:** Visualize training progress
- **GPU Acceleration:** CUDA enabled
---
## Directory Structure
FlappyBirdRL/
│
├── flappy_gym_env.py # Custom Gym env
├── train_ppo.py # PPO training script
├── train_a2c.py # A2C training script
├── entropy_annealing.py # Custom callback for entropy scheduling
├── ppo_flappybird_tensorboard/ # Logs
├── saved_models/ # Saved weights
├── README.md # This file!
---
## Installation
1. **Clone the repo**
```
git clone https://github.com/your-username/FlappyBirdRL.git
cd FlappyBirdRL
2. **Create a virtual environment (recommended)**
```
python -m venv venv
source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows
```
3. **Install dependencies**
```
pip install -r requirements.txt
```
Note: The pygame-learning-environment package is to be installed the following way:
```
git clone https://github.com/ntasfi/PyGame-Learning-Environment.git
cd PyGame-Learning-Environment
pip install -e .
```
## Train the Agent
```
python train_ppo.py
```
Edit train_ppo.py or train_a2c.py to tweak hyperparameters:
n_steps, batch_size, gamma, learning_rate
Entropy annealing: initial vs. final ent_coef
Total timesteps
## Monitor Training
```
tensorboard --logdir ppo_flappybird_tensorboard/
```
Open http://localhost:6006 in your browser to view learning curves, rewards, entropy, loss terms, etc.
## Test the Agent
```
python test_ppo.py
```
## Acknowledgements
Stable Baselines3
OpenAI Gym
Original Flappy Bird graphics by dotGBA