Demon-ball-agent

Big Ball Game AI

This model is a Deep Q-Network (DQN) agent trained to play "Big Ball Swallows Small Ball", a dynamic arcade-style game where the goal is to eat smaller balls while avoiding larger ones.

Model Description

• Input: Game state vector (69 dimensions) containing:

  • Player position (x,y)
  • Player size
  • Hunger meter
  • Nearest 13 food items' information (position, size, distance, edibility)

• Architecture:

  • Dueling DQN with Noisy Linear layers
  • Feature extraction: 2 fully connected layers (256 units each)
  • Value stream: 2 noisy linear layers (128 -> 1)
  • Advantage stream: 2 noisy linear layers (128 -> action_space)

Training

• Framework: PyTorch 2.5.1
• Training Data: Generated through gameplay (~2000 episodes)
• Infrastructure: CUDA-enabled GPU
• Training Time: ~4 hours

Training Parameters

episodes: 2000
max_steps: 1500
batch_size: 64
target_update: 100
gamma: 0.99
initial_epsilon: 1.0
final_epsilon: 0.01

Performance

The model achieves:

• Average score: ~3000 points
• Win rate: ~40%
• Average survival time: 800 steps

Limitations

• May get stuck in local optima (circular patterns)
• Performance degrades with very large numbers of food items
• Can be overly cautious with larger food items

Useage

Visit Github Repo

License

This model is released under the MIT License. See the LICENSE file for details.