SYNC: add rs_inventory_q.md from lecture-python.myst

[mmcky]

Summary

• Adds lectures/rs_inventory_q.md, mirrored from QuantEcon/lecture-python.myst at upstream commit 78030a3a2 (latest commit touching this file, via PR Add lectures on Kihlstrom's paper, Ross Recovery Theorem and update lectures in probability theory lecture-python.myst#859; originally introduced in Add risk-sensitive inventory management lecture lecture-python.myst#827).
• Second of three PRs implementing the plan agreed in TASK: reorder lectures #4. Depends on SYNC: add inventory_q.md from lecture-python.myst #19, which adds inventory_q.md (this file {doc}-references it 10 times).
• File is not yet wired into _toc.yml — only_build_toc_files: true keeps it out of the build until the TOC PR lands.
• No {cite} references; no intersphinx-prefix localizations needed.

Follow-ups

• TOC: introduce Markov Decision Processes section (closes TASK: reorder lectures #4).
• Update DOC: Primary source mapping for all lecture files #7 to bump the lecture-python.myst mapping from 25 → 27 files.

Test plan

• CI passes (file is outside the TOC, so build should be unaffected).
• After both SYNC: add inventory_q.md from lecture-python.myst #19 and this PR land, the 10 {doc} references to inventory_q resolve against a real file in main.

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[Copilot]

Pull request overview

This PR adds a new lecture, rs_inventory_q.md, introducing a risk-sensitive variant of the inventory management model and showing how to solve it via value function iteration and a risk-sensitive adaptation of Q-learning (mirrored from lecture-python.myst).

Changes:

• Add a new MyST/Jupytext lecture file on risk-sensitive inventory control with a risk-sensitive Bellman operator (log-sum-exp) and greedy policy extraction.
• Implement and demonstrate a risk-sensitive Q-factor formulation and corresponding Q-learning update rule (argmin policy).
• Include plotting, simulation, and learning-trajectory visualization cells for comparing VFI vs Q-learning outcomes.

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