CogitoNTNU/DeepTactics-Muzero
Feb 2025 – Apr 2025
3 Months active
Languages Used
PythonMarkdownShellYAMLC++Jupyter Notebook
Technical Skills
Backend DevelopmentConfiguration ManagementCI/CDCode CleanupCode RefactoringDeep Learning
BrageNTNU
PROFILE
Bragentnu
Brage H. Kvinge developed core infrastructure for the CogitoNTNU/DeepTactics-Muzero repository, focusing on unified configuration and multi-environment reinforcement learning. He introduced a centralized configuration system in Python, enabling streamlined management of game and training parameters, and extended support for both TicTacToe and CartPole environments through abstract base classes. Brage stabilized the training pipeline, implemented checkpointing, and integrated Slurm for scalable compute, while improving code quality with refactoring and linting. His work enhanced reproducibility, accelerated experimentation, and improved maintainability, leveraging skills in backend development, configuration management, and deep learning to deliver robust, extensible solutions for research and development workflows.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
31Total
Bugs
4
Commits
31
Features
19
Lines of code
6,140
Activity Months3
Your Network
37 people
Work History
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025 performance summary for CogitoNTNU/DeepTactics-Muzero: Implemented a unified game-environment abstraction and multi-environment support (TicTacToe and CartPole) with centralized configuration and a dedicated TicTacToe config function. Enhanced the MuZero training pipeline to operate across multiple environments, including a CartPole debugging config, corrected visit-probability targets, and a refactored backend training configuration with improved logging, batch sizing, and loss calculation. These changes deliver end-to-end capability for multi-environment experimentation, improving stability, observability, and throughput, and enabling faster validation of RL strategies across games.
8 Commits • 2 Features
Apr 1, 2025April 2025 performance summary for CogitoNTNU/DeepTactics-Muzero: Implemented a unified game-environment abstraction and multi-environment support (TicTacToe and CartPole) with centralized configuration and a dedicated TicTacToe config function. Enhanced the MuZero training pipeline to operate across multiple environments, including a CartPole debugging config, corrected visit-probability targets, and a refactored backend training configuration with improved logging, batch sizing, and loss calculation. These changes deliver end-to-end capability for multi-environment experimentation, improving stability, observability, and throughput, and enabling faster validation of RL strategies across games.
April 2025
March 2025
22 Commits • 16 Features
Mar 1, 2025March 2025 monthly summary for CogitoNTNU/DeepTactics-Muzero focused on stabilizing the ML pipeline, accelerating experimentation, and improving maintainability. Notable outcomes include training stabilization to prevent crashes, implementation of save/load capabilities for checkpointing, scalable compute via Slurm integration with modularized code, and comprehensive code quality improvements. The team also enhanced configuration options to support batch workflows and experimented with ELU activation for better model performance. Documentation and onboarding materials were expanded (IDUN tutorial, dummy test, and README updates), supporting faster contributions and CI reliability. These changes collectively reduce runtime risk, shorten iteration cycles, and lower long-term maintenance costs.
March 2025
22 Commits • 16 Features
Mar 1, 2025March 2025 monthly summary for CogitoNTNU/DeepTactics-Muzero focused on stabilizing the ML pipeline, accelerating experimentation, and improving maintainability. Notable outcomes include training stabilization to prevent crashes, implementation of save/load capabilities for checkpointing, scalable compute via Slurm integration with modularized code, and comprehensive code quality improvements. The team also enhanced configuration options to support batch workflows and experimented with ELU activation for better model performance. Documentation and onboarding materials were expanded (IDUN tutorial, dummy test, and README updates), supporting faster contributions and CI reliability. These changes collectively reduce runtime risk, shorten iteration cycles, and lower long-term maintenance costs.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a Unified Game and Training Configuration System for CogitoNTNU/DeepTactics-Muzero, centralizing core parameters to streamline experimentation and improve reproducibility. Implemented a dedicated configuration class that manages action space, input planes, game dimensions, and training hyperparameters, with extensibility hooks for custom environment encoding and policy functions.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a Unified Game and Training Configuration System for CogitoNTNU/DeepTactics-Muzero, centralizing core parameters to streamline experimentation and improve reproducibility. Implemented a dedicated configuration class that manages action space, input planes, game dimensions, and training hyperparameters, with extensibility hooks for custom environment encoding and policy functions.
February 2025
Activity
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Quality Metrics
Correctness84.4%
Maintainability85.2%
Architecture81.8%
Performance74.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
C++Jupyter NotebookMarkdownPythonShellYAML
Technical Skills
Abstract Base ClassesBackend DevelopmentCI/CDCode CleanupCode RefactoringConfiguration ManagementData VisualizationDebuggingDeep LearningDependency ManagementDocumentationGame DevelopmentHPCHyperparameter TuningLinting
Repositories Contributed To
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