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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language: en
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+ tags:
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+ - pygame
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+ - reinforcement-learning
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+ - dqn
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+ - game-ai
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+ license: mit
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+ datasets:
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+ - custom
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+ metrics:
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+ - accuracy
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+ - reward
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+ ---
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+
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+
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+ # Big Ball Game AI
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+
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+ 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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+
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+ ## Model Description
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+
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+ - **Input**: Game state vector (69 dimensions) containing:
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+ - Player position (x,y)
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+ - Player size
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+ - Hunger meter
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+ - Nearest 13 food items' information (position, size, distance, edibility)
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+
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+ - **Architecture**:
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+ - Dueling DQN with Noisy Linear layers
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+ - Feature extraction: 2 fully connected layers (256 units each)
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+ - Value stream: 2 noisy linear layers (128 -> 1)
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+ - Advantage stream: 2 noisy linear layers (128 -> action_space)
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+
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+ ## Training
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+
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+ - **Framework**: PyTorch 2.5.1
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+ - **Training Data**: Generated through gameplay (~2000 episodes)
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+ - **Infrastructure**: CUDA-enabled GPU
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+ - **Training Time**: ~4 hours
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+
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+ ### Training Parameters
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+
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+ ```yaml
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+ episodes: 2000
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+ max_steps: 1500
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+ batch_size: 64
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+ target_update: 100
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+ gamma: 0.99
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+ initial_epsilon: 1.0
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+ final_epsilon: 0.01
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+ ```
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+
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+ ## Performance
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+
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+ The model achieves:
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+
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+ * Average score: ~3000 points
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+ * Win rate: ~40%
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+ * Average survival time: 800 steps
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+
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+ ## Limitations
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+
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+ * May get stuck in local optima (circular patterns)
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+ * Performance degrades with very large numbers of food items
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+ * Can be overly cautious with larger food items
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+
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+ ## Useage
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+
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+ Visit [Github Repo](https://github.com/me0w00f/Big-Ball-Swallows-Small-Ball)
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+
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+ # License
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+ This model is released under the MIT License. See the LICENSE file for details.