🏆 3rd Place Winner | April 2024 | Pocket Tanks AI Competition
This repository showcases my solution for the XODIA Reinforcement Learning Competition, where I secured 3rd place among 30+ participants. The challenge involved developing an AI agent capable of mastering Pocket Tanks through optimized reward function engineering and reinforcement learning techniques.
- Design an intelligent AI bot for Pocket Tanks
- Implement an optimized reward function
- Compete against other AI agents in various scenarios
- Maximize performance and strategic decision-making
🐍 Python 3.8+
🤖 Xodia24 (Competition Framework)
🧠 stable-baselines3
🔥 PyTorch
📊 TensorBoard (Monitoring & Visualization)
☁️ Google Colab (Training Environment)
Our sophisticated reward system employs advanced mathematical modeling to optimize agent behavior:
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Advanced Mathematics
- Quadratic equations for precision control
- Linear decay patterns for predictable behavior
- Hyperbolic functions for specialized scenarios
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Distance-Based Scaling
- Dynamic reward adjustment based on target distance
- Optimized range effectiveness calculations
- Strategic positioning incentives
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Seven Bullet Types
- Standard Shell: Close combat specialist
- Triple Threat: Multi-range effectiveness
- Long Shot: Distance warfare
- Heavy Impact: Maximum damage potential
- Blast Radius: Area control
- Healing Halo: Support capabilities
- Boomerang Blast: Tactical specialty
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Strategic Design
- Engineered for tactical diversity
- Balanced risk-reward mechanics
- Situation-aware decision making
- 🥉 3rd Place Overall Ranking
- 📈 Consistent High-Performance Metrics
- 🎯 Superior Strategic Decision Making
- The XODIA organizing team for creating this challenging competition
- Fellow participants for pushing the boundaries of AI gaming
- The reinforcement learning community for valuable resources
For questions or collaboration opportunities, feel free to reach out!
Made with 🤖 and ❤️ for the XODIA Competition