footprintz/Machine-Learning-Quant — reverse-engineered prompt

Build me a hands on Jupyter notebook project that teaches reinforcement learning step by step, ending with a simple trading agent experiment.

I want it to start with a tiny grid world example for value iteration and policy iteration, then another grid world notebook for Q learning, then a deep Q learning example using Lunar Lander, and finally a stock market trading environment where an agent can choose basic actions like buy, sell, or hold and learn from rewards. Use TensorFlow 2 and an OpenAI Gym style environment if that makes sense.

Please make the notebooks readable for someone learning, with clear explanations, charts where useful, and code that can actually run. Include a small trading environment file, simple training loop, performance output, and a README that explains what each notebook does and how to install and run everything. Keep it educational, not a real money trading system. Look up current docs online if you need to.