Welcome to the Angry Birds RL Competition! 🎯🐦
This competition challenges participants to develop Reinforcement Learning (RL) agents that can efficiently launch birds to hit pigs using a physics-based environment.
The environment simulates an Angry Birds-style game where:
- A bird is launched from a slingshot towards a target pig.
- The bird follows a trajectory influenced by gravity.
- Players control the launch power and angle to maximize accuracy.
The environment provides an observation space represented as a 8-dimensional vector:
| Feature | Description | Range |
|---|---|---|
bird_x |
Bird's x-coordinate | [0, 800] |
bird_y |
Bird's y-coordinate | [0, 450] |
velocity_x |
Bird's x-axis velocity | [-30, 30] |
velocity_y |
Bird's y-axis velocity | [-30, 30] |
pig_x |
Pig's x-coordinate | [0, 800] |
pig_y |
Pig's y-coordinate | [0, 450] |
max_height |
Maximum height reached by the bird | [0, 450] |
launch_angle |
Bird's launch angle (radians) | [-π, π] |
Participants control the bird's launch power along the x and y axes:
| Action | Description | Range |
|---|---|---|
power_x |
Launch power in x-direction | [1, 15] |
power_y |
Launch power in y-direction | [1, 15] |
- A higher power_x pushes the bird farther horizontally.
- A higher power_y results in a higher arc.
Your RL agent earns rewards based on performance:
- ✅ +200 for hitting the pig.
- 🎯 +0.5 per unit decrease in distance to the pig.
- 🏹 Bonus reward for achieving interesting trajectories.
- ❌ -5 for going out of bounds.
- ⏳ Episode ends when:
- The bird hits the pig.
- The bird goes out of bounds.
- The maximum number of steps (200) is reached.
Good luck to all the participants!