RL Model Training → Performance Tracking → Research Documentation
Automate the end-to-end process of training reinforcement learning models with OpenAI Baselines, tracking their performance, and generating research documentation for ML teams.
Workflow Steps
GitHub Actions
Trigger training pipeline
Set up automated workflow that triggers when new RL training code is pushed, automatically running OpenAI Baselines A2C or ACKTR experiments with specified hyperparameters
Weights & Biases
Log training metrics
Configure W&B to automatically capture training loss, reward curves, sample efficiency metrics, and computational costs from the Baselines experiments
Jupyter Notebooks
Generate analysis reports
Use automated notebook execution to create performance comparison charts between A2C and ACKTR, highlighting sample efficiency gains and computational trade-offs
Notion
Create research documentation
Automatically populate experiment database with results, linking to W&B dashboards and generated analysis notebooks for team knowledge sharing
Workflow Flow
Step 1
GitHub Actions
Trigger training pipeline
Step 2
Weights & Biases
Log training metrics
Step 3
Jupyter Notebooks
Generate analysis reports
Step 4
Notion
Create research documentation
Why This Works
Combines automated experiment execution with comprehensive logging and documentation, ensuring no experimental insights are lost while maintaining reproducible research workflows
Best For
ML research teams running multiple RL experiments need to systematically track and document model performance comparisons
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