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Brady Chin
Mid-level AI/Robotics Engineer specializing in computer vision inspection and reinforcement learning
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Post-graduate, self-directed robotics/RL practitioner who independently built a modular reinforcement learning training framework in Python using Stable-Baselines3, Gymnasium, and PyTorch. Emphasizes reproducible experimentation (multi-seed validation), simulation (PyBullet/Box2D), and systematic comparison of algorithms/environments via a factory-pattern architecture.
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Key Strengths
- Built a modular reinforcement learning training framework as sole developer
- Designed configurable experimentation across multiple agents/environments via factory pattern
- Reproducible evaluation using multi-seed validation and performance tracking
- Hands-on simulation experience with PyBullet and Gymnasium/Box2D
- Practical RL training monitoring and diagnostics using TensorBoard and structured logging
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