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.
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Input: Game state vector (69 dimensions) containing:
- Player position (x,y)
- Player size
- Hunger meter
- Nearest 13 food items' information (position, size, distance, edibility)
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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)
1episodes: 2000
2max_steps: 1500
3batch_size: 64
4target_update: 100
5gamma: 0.99
6initial_epsilon: 1.0
7final_epsilon: 0.01
This model is released under the MIT License. See the LICENSE file for details.
