assume-framework/assume
Oct 2024 – Oct 2025
12 Months active
Languages Used
PythonSQLJSONJupyter NotebookRSTYAMLreStructuredTextrst
Technical Skills
API DevelopmentBackend DevelopmentData AnalysisData EngineeringData ProcessingDebugging
kim-mskw
PROFILE
Kim-mskw
Kim Miskiw developed and refined advanced reinforcement learning and market simulation features for the assume-framework/assume repository over a twelve-month period. Leveraging Python, PyTorch, and Jupyter Notebooks, Kim engineered robust RL strategies, improved data handling, and enhanced dashboard visualizations to support energy market and storage system modeling. Their work included algorithm refactoring, code cleanup, and the introduction of configurable activation functions, which improved model flexibility and reliability. By focusing on documentation, onboarding, and test coverage, Kim ensured maintainable, production-ready code. The depth of their contributions addressed both technical complexity and usability, resulting in a more extensible and stable software platform.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
66Total
Bugs
3
Commits
66
Features
23
Lines of code
29,078
Activity Months12
Your Network
52 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered the Default Portfolio bidding strategy (default_portfolio) in assume-framework/assume, replacing the unimplemented portfolio_learning strategy. Performed code cleanups, initialization renames, and updated release notes to document the feature and deprecation. These changes reduce ambiguity, improve maintainability, and provide clearer onboarding and customer-facing documentation.
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered the Default Portfolio bidding strategy (default_portfolio) in assume-framework/assume, replacing the unimplemented portfolio_learning strategy. Performed code cleanups, initialization renames, and updated release notes to document the feature and deprecation. These changes reduce ambiguity, improve maintainability, and provide clearer onboarding and customer-facing documentation.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for assume-framework/assume: Delivered Activation Function Configuration and Actor Output Range Handling with a robust, configurable activation mechanism and fixed clamping logic. Replaced implicit extreme value calculations with explicit dictionary lookups for activation functions, enabling precise output ranges and easier experimentation. Fixed a bug where action clamping used extreme values from the activation function instead of the forward-pass min/max, addressing incorrect outputs under Xavier initialization. Commits include reverting the implicit extreme value calculation (9b147bb3782986207524d7fe339e97ad65898d4d) and adding release notes (6f10eeda029698e8e3a96548062dbc5575e9310f). Release notes documenting the changes were published to accompany this work.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for assume-framework/assume: Delivered Activation Function Configuration and Actor Output Range Handling with a robust, configurable activation mechanism and fixed clamping logic. Replaced implicit extreme value calculations with explicit dictionary lookups for activation functions, enabling precise output ranges and easier experimentation. Fixed a bug where action clamping used extreme values from the activation function instead of the forward-pass min/max, addressing incorrect outputs under Xavier initialization. Commits include reverting the implicit extreme value calculation (9b147bb3782986207524d7fe339e97ad65898d4d) and adding release notes (6f10eeda029698e8e3a96548062dbc5575e9310f). Release notes documenting the changes were published to accompany this work.
July 2025
14 Commits • 2 Features
Jul 1, 2025July 2025 — Key deliverables in assume-framework/assume: RL education enhancements and documentation cleanup for tutorials 04a/04b, Storage RL and DSM tutorials with updated energy-cost modeling and release notes, and a stability fix for RL example 09. These work improve onboarding, repeatable experiments, and economics accuracy, underpinned by targeted commits across docs, tutorials, and tests.
14 Commits • 2 Features
Jul 1, 2025July 2025 — Key deliverables in assume-framework/assume: RL education enhancements and documentation cleanup for tutorials 04a/04b, Storage RL and DSM tutorials with updated energy-cost modeling and release notes, and a stability fix for RL example 09. These work improve onboarding, repeatable experiments, and economics accuracy, underpinned by targeted commits across docs, tutorials, and tests.
July 2025
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 summary for assume-framework/assume highlights feature delivery and code quality improvements in the Learning Strategies area. Delivered a comprehensive refactor of learning strategies with refined initial exploration action calculation, improved noise handling, clearer test configuration, and release notes. Established base-class utilities to support future extensibility. Minor test config cleanup and reviewer feedback addressed contributed to higher quality and stability.
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 summary for assume-framework/assume highlights feature delivery and code quality improvements in the Learning Strategies area. Delivered a comprehensive refactor of learning strategies with refined initial exploration action calculation, improved noise handling, clearer test configuration, and release notes. Established base-class utilities to support future extensibility. Minor test config cleanup and reviewer feedback addressed contributed to higher quality and stability.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 performance summary for assume-framework/assume: Consolidated and expanded the continue_learning workflow with a new single-bid RL strategy, improved multi-agent support, and clearer strategy loading. Core changes include refactored weight transfer logic for varying agent counts, enhanced error handling, and more readable docstrings and release notes. Documentation and onboarding were tightened with updated learning-algorithms notes. These changes enable more flexible RL experiments, reduce operational risk, and accelerate integration of new strategies.
2 Commits • 1 Features
May 1, 2025May 2025 performance summary for assume-framework/assume: Consolidated and expanded the continue_learning workflow with a new single-bid RL strategy, improved multi-agent support, and clearer strategy loading. Core changes include refactored weight transfer logic for varying agent counts, enhanced error handling, and more readable docstrings and release notes. Documentation and onboarding were tightened with updated learning-algorithms notes. These changes enable more flexible RL experiments, reduce operational risk, and accelerate integration of new strategies.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025In April 2025, the team focused on improving reliability and maintainability of the assume-framework/assume project. Key changes included a bug fix to the Reinforcement Learning Utility and a minor feature/documentation update to ElasticDemandStrategy. The work emphasizes clarity, faster debugging, and reduced onboarding time for new engineers, delivering measurable business value and strengthening code quality across critical RL components.
April 2025
2 Commits • 1 Features
Apr 1, 2025In April 2025, the team focused on improving reliability and maintainability of the assume-framework/assume project. Key changes included a bug fix to the Reinforcement Learning Utility and a minor feature/documentation update to ElasticDemandStrategy. The work emphasizes clarity, faster debugging, and reduced onboarding time for new engineers, delivering measurable business value and strengthening code quality across critical RL components.
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025: Delivered targeted documentation and stability improvements for assume-framework/assume, focusing on StorageRLStrategy action-value mapping, RL data handling safeguards against data loss, and notebook/RL operator readability. These changes improve maintainability, reduce operational risk, and lay groundwork for production-grade RL workflows. Key improvements include documentation clarifications, data-length mismatch warnings, and precommit-driven quality enhancements.
8 Commits • 3 Features
Mar 1, 2025March 2025: Delivered targeted documentation and stability improvements for assume-framework/assume, focusing on StorageRLStrategy action-value mapping, RL data handling safeguards against data loss, and notebook/RL operator readability. These changes improve maintainability, reduce operational risk, and lay groundwork for production-grade RL workflows. Key improvements include documentation clarifications, data-length mismatch warnings, and precommit-driven quality enhancements.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 Monthly Summary for assume-framework/assume: Delivered UI-focused improvements to the dashboard, reintroduced noise visualization in actions, and clarified action descriptions to enhance UX and decision-making. No major bug fixes were required this month; the emphasis was on UI polish and stability to support upcoming features.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 Monthly Summary for assume-framework/assume: Delivered UI-focused improvements to the dashboard, reintroduced noise visualization in actions, and clarified action descriptions to enhance UX and decision-making. No major bug fixes were required this month; the emphasis was on UI polish and stability to support upcoming features.
January 2025
3 Commits • 3 Features
Jan 1, 2025January 2025: Three key deliverables for assume-framework/assume focusing on clarity, efficiency, and documentation. Dashboard refactor sharpens data visualization by displaying unit/episodic plots only; Reinforcement Learning system improvements enhance PyTorch compatibility, actor/critic networks, and multi-agent training; Release notes updated to cover Tensorboard integration and min-max scaling, improving release transparency and learnability.
3 Commits • 3 Features
Jan 1, 2025January 2025: Three key deliverables for assume-framework/assume focusing on clarity, efficiency, and documentation. Dashboard refactor sharpens data visualization by displaying unit/episodic plots only; Reinforcement Learning system improvements enhance PyTorch compatibility, actor/critic networks, and multi-agent training; Release notes updated to cover Tensorboard integration and min-max scaling, improving release transparency and learnability.
January 2025
December 2024
9 Commits • 3 Features
Dec 1, 2024Month 2024-12: Delivered RL strategy and scaling improvements for assume-framework/assume. Focused on business value: more stable RL pricing decisions, faster training cycles through precomputed scaling, and clearer documentation to aid onboarding and maintenance. Also performed targeted bug fixes to improve reliability.
December 2024
9 Commits • 3 Features
Dec 1, 2024Month 2024-12: Delivered RL strategy and scaling improvements for assume-framework/assume. Focused on business value: more stable RL pricing decisions, faster training cycles through precomputed scaling, and clearer documentation to aid onboarding and maintenance. Also performed targeted bug fixes to improve reliability.
November 2024
12 Commits • 4 Features
Nov 1, 2024November 2024 (assume-framework/assume) — concise monthly summary highlighting key features delivered, major bugs fixed, and overall impact. Key outcomes include more accurate market clearing price extraction, robust redispatch tests, standardized notebook outputs for reproducibility, and improved documentation and dependency management for Tutorial 10. These changes enhance reliability of simulations, accelerate onboarding, and support maintainable, deliverable code for Tutorial 06 and 10 scenarios.
12 Commits • 4 Features
Nov 1, 2024November 2024 (assume-framework/assume) — concise monthly summary highlighting key features delivered, major bugs fixed, and overall impact. Key outcomes include more accurate market clearing price extraction, robust redispatch tests, standardized notebook outputs for reproducibility, and improved documentation and dependency management for Tutorial 10. These changes enhance reliability of simulations, accelerate onboarding, and support maintainable, deliverable code for Tutorial 06 and 10 scenarios.
November 2024
October 2024
9 Commits • 2 Features
Oct 1, 2024In 2024-10, delivered key flow data capabilities in assume-framework/assume, focusing on flow data collection/integration for market clearing and robust output formatting, with enhanced test coverage and release-focused fixes. Implemented flow data storage during complex clearing, integrated with extraction, and ensured compatibility with multiple solver outputs and contract roles. Improved observability through optional flow logging and a dedicated performance test path comparing with/without logging. Strengthened validation around flow data outputs to support different data formats and Pyomo model flows.
October 2024
9 Commits • 2 Features
Oct 1, 2024In 2024-10, delivered key flow data capabilities in assume-framework/assume, focusing on flow data collection/integration for market clearing and robust output formatting, with enhanced test coverage and release-focused fixes. Implemented flow data storage during complex clearing, integrated with extraction, and ensured compatibility with multiple solver outputs and contract roles. Improved observability through optional flow logging and a dedicated performance test path comparing with/without logging. Strengthened validation around flow data outputs to support different data formats and Pyomo model flows.
Activity
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Quality Metrics
Correctness84.6%
Maintainability86.4%
Architecture80.4%
Performance74.8%
AI Usage21.4%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonRSTSQLYAMLreStructuredTextrst
Technical Skills
API DevelopmentAlgorithm RefactoringBackend DevelopmentCode CleanupCode DocumentationCode ExplanationCode FormattingCode RefactoringCode StyleComment EnhancementConfiguration ManagementDashboard DevelopmentData AnalysisData CleaningData Engineering
Repositories Contributed To
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