assume-framework/assume
Nov 2025 – Dec 2025
2 Months active
Languages Used
JSONPythonMarkdownreStructuredText
Technical Skills
GPU programmingMachine LearningPythonPython programmingReinforcement Learningback end development
mthede
PROFILE
Mthede
Worked on the assume-framework/assume repository, delivering a centralized learning configuration system using Python dataclasses to support multiple simulation modes and improve experiment reproducibility. Refactored backend and dashboard components to enhance policy loading accuracy, enable GPU utilization during evaluation, and improve data visibility for multi-agent actions. Improved robustness by restructuring configuration files, adding error handling, and updating documentation for clarity and onboarding. Enhanced Jupyter notebook workflows by refactoring image path handling with pathlib and standardizing documentation language. Demonstrated skills in Python, data visualization, and reinforcement learning, focusing on reliability, maintainability, and streamlined experimental workflows for machine learning research environments.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
15Total
Bugs
1
Commits
15
Features
7
Lines of code
2,123
Activity Months2
Your Network
61 peopleSame Organization
@kit.edu43
Adrian HerrmannMember
Adrien DamseauxMember
ar0305Member
Arne LangeMember
Artur GottmannMember
Benedikt JutzMember
Chia-Lin LinMember
Christian ChwalaMember
DrCWSMember
Work History
December 2025
6 Commits • 3 Features
Dec 1, 2025December 2025: Delivered substantive robustness and usability improvements to assume-framework/assume. Implemented storage learning robustness and configuration refactor: moved learning_mode under learning_config, updated docs and docstrings, and ensured notebook compatibility. Enhanced notebook reliability by refactoring image path handling to pathlib and improving image display in Jupyter notebooks. Strengthened documentation quality with American English consistency and spelling fixes, improving onboarding and long-term maintainability. These changes reduce configuration errors, improve developer experience, and support more reliable experimental workflows.
6 Commits • 3 Features
Dec 1, 2025December 2025: Delivered substantive robustness and usability improvements to assume-framework/assume. Implemented storage learning robustness and configuration refactor: moved learning_mode under learning_config, updated docs and docstrings, and ensured notebook compatibility. Enhanced notebook reliability by refactoring image path handling to pathlib and improving image display in Jupyter notebooks. Strengthened documentation quality with American English consistency and spelling fixes, improving onboarding and long-term maintainability. These changes reduce configuration errors, improve developer experience, and support more reliable experimental workflows.
December 2025
November 2025
9 Commits • 4 Features
Nov 1, 20252025-11 Monthly Summary — assume-framework/assume Key features delivered: - LearningConfig system overhaul: Introduced LearningConfig dataclass and centralized learning configuration, enabling four simulation modes (no RL, single run with loaded RL strategies, training run, continue learning) with comprehensive refactors across learning strategies. - Learning dashboard enhancements: Corrected action display for multiple actions per agent; updated plugin version, dashboard ID, and tooltips for clearer data visibility. - Simulation policy loading improvement: Switched final runs to load the latest trained policies for higher accuracy, replacing the previous average-reward policy usage. - Allow GPU during evaluation: Removed GPU usage restrictions during evaluation to enable potential performance gains in learning tasks. - Enforce required learning role for learning strategy: Bug fix to ensure a learning role is specified when using a learning strategy to prevent misconfiguration and runtime errors. Major bugs fixed: - Enforce required learning role for learning strategy (missing learning_config) to prevent runtime misconfigurations. - Learning dashboard action chart: Fixed display of multiple actions per agent to improve data visibility. Overall impact and accomplishments: - Strengthened configuration reliability and experiment reproducibility through centralized LearningConfig and robust validation. - Improved result accuracy and performance with latest-policy loading and GPU-enabled evaluation. - Enhanced observability and troubleshooting via dashboard enhancements, enabling faster decision-making and iteration. Technologies/skills demonstrated: - Python dataclasses and config management, refactoring for modular learning strategies, pre-commit/test hygiene, policy loading logic, GPU utilization in evaluation, and dashboard/data-visibility improvements.
November 2025
9 Commits • 4 Features
Nov 1, 20252025-11 Monthly Summary — assume-framework/assume Key features delivered: - LearningConfig system overhaul: Introduced LearningConfig dataclass and centralized learning configuration, enabling four simulation modes (no RL, single run with loaded RL strategies, training run, continue learning) with comprehensive refactors across learning strategies. - Learning dashboard enhancements: Corrected action display for multiple actions per agent; updated plugin version, dashboard ID, and tooltips for clearer data visibility. - Simulation policy loading improvement: Switched final runs to load the latest trained policies for higher accuracy, replacing the previous average-reward policy usage. - Allow GPU during evaluation: Removed GPU usage restrictions during evaluation to enable potential performance gains in learning tasks. - Enforce required learning role for learning strategy: Bug fix to ensure a learning role is specified when using a learning strategy to prevent misconfiguration and runtime errors. Major bugs fixed: - Enforce required learning role for learning strategy (missing learning_config) to prevent runtime misconfigurations. - Learning dashboard action chart: Fixed display of multiple actions per agent to improve data visibility. Overall impact and accomplishments: - Strengthened configuration reliability and experiment reproducibility through centralized LearningConfig and robust validation. - Improved result accuracy and performance with latest-policy loading and GPU-enabled evaluation. - Enhanced observability and troubleshooting via dashboard enhancements, enabling faster decision-making and iteration. Technologies/skills demonstrated: - Python dataclasses and config management, refactoring for modular learning strategies, pre-commit/test hygiene, policy loading logic, GPU utilization in evaluation, and dashboard/data-visibility improvements.
Activity
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Quality Metrics
Correctness92.0%
Maintainability88.0%
Architecture86.6%
Performance86.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
JSONMarkdownPythonreStructuredText
Technical Skills
Data VisualizationGPU programmingJupyter NotebooksMachine LearningPythonPython ProgrammingPython programmingReinforcement Learningback end developmentbackend developmentdashboard developmentdata analysisdata visualizationdocumentationerror handling
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