Real-World Reinforcement Learning Model

I need a working reinforcement learning model trained and validated on a real-world dataset that I will supply. Your task covers everything from choosing a suitable algorithm, preprocessing the data, setting up the training loop, to delivering a reproducible codebase with clear instructions so I can retrain or fine-tune later. You are free to work in Python with frameworks such as PyTorch or TensorFlow—whichever you feel is the best fit—provided the final solution runs on a standard GPU workstation. I will provide the dataset and explain the reward structure; from there I expect you to handle environment creation (if needed), hyper-parameter tuning, logging, and evaluation metrics that demonstrate learning progress and final performance. Deliverables will include: • Fully commented source code • A short report (or notebook) explaining the architecture, training process, and results • Instructions for replicating the experiment on my machine I am happy to discuss finer details like exploration strategies, baseline comparisons, or integration with existing systems once we start.