Metta-AI/metta
Aug 2025 – Aug 2025
1 Month active
Languages Used
Python
Technical Skills
Agent TrainingData VisualizationEnvironment SimulationJupyter NotebooksPythonReinforcement Learning
Brandon Harvey
PROFILE
Brandon Harvey
Developed a foundational reinforcement learning onboarding notebook for the Metta-AI/metta repository, delivering an end-to-end workflow that guides users through environment setup, agent training, and evaluation within a simple gridworld simulation. The work leveraged Python, Jupyter Notebooks, and core reinforcement learning techniques to create a reproducible reference implementation, enabling new contributors to quickly understand and experiment with RL concepts. By integrating environment simulation and data visualization, the notebook demonstrates the process of observing and evaluating trained agents. This contribution established a clear, practical entry point for onboarding and set the stage for future expansion of reinforcement learning features in the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,832
Activity Months1
Your Network
37 peopleShared Repositories
37
Axel KerbecMember
al-doesMember
Brandon HarveyMember
daphnedemekasMember
David BloominMember
Dhaya LakshminarayananMember
Dominik FarrMember
Gabriel KeithMember
George DeaneMember
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Metta-AI/metta: Delivered a foundational reinforcement learning onboarding notebook and prepared ground for broader RL experiments. This work provides an end-to-end RL workflow example, from setup to training and evaluation, including an in-gridworld agent demonstration to showcase observing and evaluating a trained agent. The deliverable serves as a reproducible reference for onboarding, demonstrations, and future feature expansion with RL concepts.
1 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Metta-AI/metta: Delivered a foundational reinforcement learning onboarding notebook and prepared ground for broader RL experiments. This work provides an end-to-end RL workflow example, from setup to training and evaluation, including an in-gridworld agent demonstration to showcase observing and evaluating a trained agent. The deliverable serves as a reproducible reference for onboarding, demonstrations, and future feature expansion with RL concepts.
August 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Agent TrainingData VisualizationEnvironment SimulationJupyter NotebooksPythonReinforcement Learning
Repositories Contributed To
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