autoppia/autoppia_iwa
Nov 2025 – Nov 2025
1 Month active
Languages Used
NonePython
Technical Skills
Data ProcessingMachine LearningPythonReinforcement Learningdata managementgit
bagus0315
PROFILE
Bagus0315
Asamieto developed a reinforcement learning PPO training pipeline for the autoppia/autoppia_iwa repository, focusing on reproducibility and maintainability. They integrated a score-model-based reward and Behavior Cloning warm-start to improve sample efficiency and training stability, while adding CLI tools for data fetching and leaderboard solution replay. Using Python and leveraging skills in data processing and machine learning, Asamieto also addressed repository hygiene by removing large artifacts from version control, tightening the .gitignore, and cleaning historical data files. This work resulted in a leaner, more reproducible codebase, facilitating efficient ML experimentation and smoother onboarding for engineers working with reinforcement learning workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
110,498
Activity Months1
Your Network
15 peopleShared Repositories
15
Work History
November 2025
4 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a robust Reinforcement Learning PPO training pipeline in autoppia/autoppia_iwa, integrating a score-model-based reward, Behavior Cloning warm-start, and CLI tooling for data fetch and leaderboard replay to strengthen the end-to-end RL workflow. Performed repository hygiene to remove large artifacts from version control, tightening .gitignore and ensuring a lean, reproducible codebase. These changes enhance training reproducibility, reduce repo size, and improve maintainability for ML experimentation and deployment.
4 Commits • 1 Features
Nov 1, 2025November 2025: Delivered a robust Reinforcement Learning PPO training pipeline in autoppia/autoppia_iwa, integrating a score-model-based reward, Behavior Cloning warm-start, and CLI tooling for data fetch and leaderboard replay to strengthen the end-to-end RL workflow. Performed repository hygiene to remove large artifacts from version control, tightening .gitignore and ensuring a lean, reproducible codebase. These changes enhance training reproducibility, reduce repo size, and improve maintainability for ML experimentation and deployment.
November 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture95.0%
Performance95.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
NonePython
Technical Skills
Data ProcessingMachine LearningPythonReinforcement Learningdata managementgitrepository managementversion control
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