OpenHUTB/nn
Sep 2025 – Dec 2025
4 Months active
Languages Used
MarkdownPythonCMakeXML
Technical Skills
Autonomous DrivingData ManipulationData VisualizationDeep Q-Networks (DQN)DocumentationMatplotlib
joe-justin369
PROFILE
Joe-justin369
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
20Total
Bugs
2
Commits
20
Features
5
Lines of code
12,461
Activity Months4
Your Network
159 people
Work History
December 2025
12 Commits • 1 Features
Dec 1, 202512月2025月度工作摘要 - OpenHUTB/nn: 自主驾驶导航与训练栈实现与稳定性提升，覆盖从路径规划到避障的端到端能力，结合强化学习、模仿学习及多目标优化，显著提升仿真学习效率与策略鲁棒性。
12 Commits • 1 Features
Dec 1, 202512月2025月度工作摘要 - OpenHUTB/nn: 自主驾驶导航与训练栈实现与稳定性提升，覆盖从路径规划到避障的端到端能力，结合强化学习、模仿学习及多目标优化，显著提升仿真学习效率与策略鲁棒性。
December 2025
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 focused on stabilizing and improving the DQN project in OpenHUTB/nn. Delivered a core algorithm and project structure refactor that consolidates environment configuration, hyperparameters, model, tests, and the main program, improving readability, maintainability, and experimentation speed. Key changes include relocating AD_DQN sources from src/ to the repository root, renaming Main.py to main.py, and updating the README and dependency declarations to align with the new structure. In parallel, increased code quality through standardized documentation: corrected erroneous comments and replaced with Chinese annotations to match team standards. These changes reduce onboarding time, enable more robust CI/tests, and set a solid foundation for future enhancements.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 focused on stabilizing and improving the DQN project in OpenHUTB/nn. Delivered a core algorithm and project structure refactor that consolidates environment configuration, hyperparameters, model, tests, and the main program, improving readability, maintainability, and experimentation speed. Key changes include relocating AD_DQN sources from src/ to the repository root, renaming Main.py to main.py, and updating the README and dependency declarations to align with the new structure. In parallel, increased code quality through standardized documentation: corrected erroneous comments and replaced with Chinese annotations to match team standards. These changes reduce onboarding time, enable more robust CI/tests, and set a solid foundation for future enhancements.
October 2025
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for OpenHUTB/nn: Delivered foundational scaffolding and training workflow for Autonomous Driving DQN, enabling rapid experimentation in CARLA. Refactored directory structure, introduced core modules for environment simulation, DQN agent, hyperparameters, and testing; added environment setup, dependency management, and a training script to bootstrap development. Achieved a reproducible baseline and laid groundwork for end-to-end autonomous driving experiments, improving onboarding, collaboration, and experimentation speed.
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for OpenHUTB/nn: Delivered foundational scaffolding and training workflow for Autonomous Driving DQN, enabling rapid experimentation in CARLA. Refactored directory structure, introduced core modules for environment simulation, DQN agent, hyperparameters, and testing; added environment setup, dependency management, and a training script to bootstrap development. Achieved a reproducible baseline and laid groundwork for end-to-end autonomous driving experiments, improving onboarding, collaboration, and experimentation speed.
October 2025
September 2025
3 Commits • 2 Features
Sep 1, 2025Monthly summary for OpenHUTB/nn - 2025-09 focusing on key features delivered, major fixes, and impact. The month delivered concrete enhancements in data manipulation, visualization, and autonomous driving experimentation, along with improved documentation visibility. The work emphasizes business value through enabling faster analysis, robust experiments, and clearer onboarding for contributors.
September 2025
3 Commits • 2 Features
Sep 1, 2025Monthly summary for OpenHUTB/nn - 2025-09 focusing on key features delivered, major fixes, and impact. The month delivered concrete enhancements in data manipulation, visualization, and autonomous driving experimentation, along with improved documentation visibility. The work emphasizes business value through enabling faster analysis, robust experiments, and clearer onboarding for contributors.
Activity
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Quality Metrics
Correctness82.0%
Maintainability82.0%
Architecture81.0%
Performance80.0%
AI Usage44.0%
Skills & Technologies
Programming Languages
CMakeMarkdownPythonXML
Technical Skills
Autonomous DrivingAutonomous Driving SimulationCARLA SimulatorComputer VisionData AnalysisData ManipulationData VisualizationDeep LearningDeep Q-NetworksDeep Q-Networks (DQN)Dependency ManagementDocumentationKerasMachine LearningMatplotlib
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