I'm a third-year undergraduate at Guangzhou University, majoring in Network Engineering with a focus on AI systems. My research sits at the intersection of reinforcement learning, embodied AI, and efficient model deployment — closing the gap between RL research and real-world robot and edge hardware.

I'm applying for PhD programs (Fall 2027) in Embodied AI / Reinforcement Learning / Robotics.

🔗 Academic Homepage  |  📄 CV  |  📧 ldz@e.gzhu.edu.cn

Research Interests:

• Reinforcement learning for embodied agents (off-policy algorithms, sample efficiency)
• Cross-platform RL model deployment (GPU / Jetson / CPU)
• Model compression and quantization for RL policies (INT8 / FP16)
• AI safety and adversarial robustness

Featured Project — rl-deploy-bench

A cross-platform RL model deployment and performance benchmarking toolkit. Export Stable-Baselines3 policies to ONNX/TorchScript, build TensorRT engines with FP16/INT8 quantization, and benchmark latency, throughput, and accuracy across x86 GPU, NVIDIA Jetson, and CPU — all from one config-driven CLI with auto-generated HTML reports.

Research value: Provides a reproducible benchmark for studying the accuracy-latency tradeoff of RL policy quantization across hardware platforms.

RL and Learning — PyTorch · Stable-Baselines3 · Gymnasium · MuJoCo Deployment — ONNX · ONNX Runtime · TensorRT · TorchScript · INT8/FP16 quantization Edge and Robotics — NVIDIA Jetson (Xavier/Orin) · ROS · CUDA · cuDNN Systems — Python · C++ · Linux · Docker · Git · CI/CD