I have a complete Python implementation of a grid-based path-planning system that blends classic A* search with my own Modified-Reward Q-Learning (MR-QLearning) for autonomous drone navigation in search-and-rescue scenarios. Alongside the code, I provide a report that details the approach, simulation setup, and benchmark results. What I need now is a meticulous technical audit that answers one overarching question: “Does the code do exactly what the report claims?” Your review should probe three areas in depth—algorithm correctness, runtime efficiency, and the clarity/accuracy of inline documentation. In particular, the simulation and results section of the report must be cross-checked against fresh runs of the code. I expect an exact, line-by-line comparison of key metrics (path cost, convergence speed, reward curves, success rates, heat-map visuals, etc.), with any discrepancies quantified and explained. If the implementation can be tightened or accelerated, point that out and illustrate why. Deliverables to be discussed. Acceptance criteria • All original results are either replicated within an agreed tolerance or the divergence is fully justified. • Every algorithmic step in the code can be mapped unambiguously to the report’s equations and pseudocode. • Suggestions are feasible, technically sound, and improve either accuracy or speed. The project is self-contained—just install the usual Python scientific stack (NumPy, jupyter, SciPy, Matplotlib, perhaps gym-like utilities) and run the scripts. I will share the source code and report as soon as we start.