Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+tags:
+- pygame
+- reinforcement-learning
+- dqn
+- game-ai
+license: mit
+datasets:
+- custom
+metrics:
+- accuracy
+- reward
+---
+
+
+# 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
+
+```yaml
+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](https://github.com/me0w00f/Big-Ball-Swallows-Small-Ball)
+
+# License
+This model is released under the MIT License. See the LICENSE file for details.