The Inventory Management with RL is a machine learning project that uses Trained policy export and Reward and state design, built and evaluated with Episode logging. It follows a reproducible, modular workflow where data preparation, model training, evaluation, and serving stay clearly separated. The project emphasizes solid engineering alongside modelling, so it can be extended, retrained, and presented confidently as a final year capstone.
Existing approaches to this problem are often manual, inconsistent, and unable to scale with larger or noisier data. Without a data-driven system built on Reward and state design and Episode logging, decisions are slow and prone to bias, and there is no structured way to measure performance or adapt to changing patterns over time.
This project develops an end-to-end ML solution that ingests data through Trained policy export, builds and tunes a model with Episode logging, and exposes results through Training loop. The workflow is designed for reproducibility and easy retraining, with clear evaluation metrics and logging. It delivers faster, consistent, data-backed results and can be extended to related problems with minimal changes.
Episode logging
OpenAI Gym / Gymnasium
Python 3.11+
Reproducible environment (requirements lock)
Structured logging and error handling
Configuration-driven settings
PyTorch / TensorFlow
Reproducible pipeline around Trained policy export and Reward and state design
Configurable model and evaluation settings
Clear metrics, plots, and logging
Modular, reusable components
Documented API or interface for results
Easy retraining with new data
The project is organized in layers: the data layer prepares and validates inputs through Trained policy export; the modelling layer trains and tunes the model with Episode logging and Reward and state design; and the serving layer exposes predictions via Training loop. Shared configuration, evaluation, and logging modules support every layer, and the whole pipeline is version-controlled and reproducible. This structure makes the project easy to debug, benchmark, and extend.
Set up the Python environment, project structure, and configuration files.
Acquire and clean the dataset, building the data layer with Trained policy export.
Perform feature engineering and build the modelling pipeline with Episode logging.
Train, tune, and evaluate the model, integrating Reward and state design for validation.
Build the serving layer with Training loop and connect end-to-end flows.
Write tests, add logging, and document results with visualizations.
Package the project and prepare the demo and viva report.
Master end-to-end machine learning project workflows
Apply Training stable RL agents and Reward shaping on real data
Build reproducible and maintainable ML code
Evaluate models with the right metrics
Present and defend a complete ML project in viva
Serve the model as a REST API for real-time predictions
Add automated retraining and drift monitoring
Containerize and deploy with CI/CD
Integrate explainability for every prediction
The Inventory Management with RL demonstrates a professional, end-to-end machine learning approach — from data and modelling to evaluation and serving. It is reproducible, practical, and easy to present, making it a strong final year ML project that stands out to examiners and recruiters alike.
