🌐 [translation-sync] [mccall_q.md] Update np.random → Generator API

[mmcky]

Automated Translation Sync

This PR contains automated translations from QuantEcon/lecture-python.myst.

Source PR

#959 - [mccall_q.md] Update np.random → Generator API

Files Updated

• ✏️ lectures/mccall_q.md
• ✏️ .translate/state/mccall_q.md.yml

Details

• Source Language: en
• Target Language: zh-cn
• Model: claude-sonnet-5

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This PR was created automatically by the translation action.

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✅ Translation Quality Review

Verdict: PASS | Model: claude-sonnet-5 | Date: 2026-07-31
Routing: editor — 2 minor finding(s) in gating categories (accuracy/terminology/syntax/diff-check/other); terminology 8 below floor 9

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📝 Translation Quality

Criterion	Score
Accuracy	9/10
Fluency	9/10
Terminology	8/10
Formatting	9/10
Overall	8.8/10

Summary: The translation of the Overview and Q-Learning sections is accurate, fluent, and largely faithful to the source, with correct handling of complex mathematical exposition and MyST formatting. Minor issues include a slight terminology conflation between 'gain parameter' and 'learning rate', a small restructuring in the Overview that adds minor interpretive phrasing not in the source, and a somewhat fragmented sentence describing the experimentation mechanism. None of these rise to the level of accuracy-threatening errors, and no syntax errors were found. Technical and mathematical content in the Overview and Q-Learning sections is accurately preserved, including all LaTeX equations and cross-references. Key terminology (Q-函数, Q-表, 时序差分学习, 时序差分误差, ε-贪婪算法) is applied consistently and matches standard usage in the field. Code comments were appropriately translated where relevant (e.g., progress print statements, docstrings) without altering code logic. Long, complex English sentences in the Q-Learning section are competently broken into readable Chinese sentences while preserving technical precision.

Suggestions:

• [minor · accuracy] lectures/mccall_q.md — ## Overview, '在本讲中，我们将Q学习算法应用到McCall求职模型中...': The English source has two distinct sentences: one stating the lecture applies Q-learning to the McCall worker's situation, and another (separate) sentence noting the lecture also considers the quit option. The translation merges the first sentence with added interpretive content ('探讨劳动者如何通过这种方法做出最优决策') not present in the source, and restructures rather than directly translating both sentences. → 本讲将Q学习算法应用于McCall劳动者所面临的情形。本讲还考虑了McCall劳动者被赋予选择辞去当前工作的情况。
• [minor · terminology] lectures/mccall_q.md — ## Q-Learning, 'A quality function...gain**参数': The English term 'gain parameter' is translated as '学习率' (learning rate), which conflates it with the separate 'lr'/'learning rate α' parameter used later in the code and text. This creates a terminology inconsistency between the informal 'gain' concept in eq:old106 and the formally named learning rate α introduced afterward. → 使用「增益参数」或保留「gain 参数」而不是「学习率」参数，以避免与后文的学习率 α 混淆
• [minor · fluency] lectures/mccall_q.md — ## Q-Learning, '来添加这一特征。': The sentence describing random experimentation is split awkwardly across a long passage with two block equations pairs, ending with a short trailing clause '来添加这一特征' that reads as fragmented compared to the more integrated original English sentence structure. → Consider restructuring to make the connection between the experimentation description and the addition to the algorithm clearer, e.g., integrating the introductory clause with the final summary sentence.
• [nit · terminology] lectures/mccall_q.md — ## Q-Learning, pseudocode step 4-5: Minor inconsistency: 'draw a new state' in step 5 is translated somewhat freely as '抽取新的状态', which is fine, but the parallel construction with step 2 ('从F中抽取初始工资报价w') could be made more consistent by using the same verb phrasing for 'draw'. → No major change needed; this is a very minor stylistic note.

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🔍 Diff Quality

Check	Status
Scope Correct	✅
Position Correct	✅
Structure Preserved	✅
Heading-map Correct	✅
Overall	10/10

Summary: The RNG refactor (np.random.seed -> np.random.default_rng passed explicitly) was correctly and completely mirrored in the Chinese translation at all matching locations, with structure and heading metadata unaffected.

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This review was generated automatically by action-translation review mode.

[Copilot]

Pull request overview

This automated translation-sync PR updates the Chinese lecture mccall_q.md to match upstream changes that migrate legacy np.random.* usage toward the newer np.random.Generator API, and updates the translation sync state metadata accordingly.

Changes:

• Replaced global np.random.seed(...) / np.random.random() usage with a rng = np.random.default_rng(...) and rng.random() in the Q-learning section.
• Threaded the rng object through Q-learning helper methods and the epoch runner.
• Updated translation sync state (source-sha, synced-at, mode, tool-version).

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
lectures/mccall_q.md	Migrates randomness in the Q-learning example toward the Generator API by introducing and passing an rng.
.translate/state/mccall_q.md.yml	Updates translation-sync bookkeeping metadata (source SHA/date/mode/tool version).

Suppressed comments (4)

lectures/mccall_q.md:570

• run_epochs 被 @jit 编译后，目前把 rng 传入并调用 qlmc.run_one_epoch(qtable, rng)，这同样会将 Generator 传入 jitclass 方法。若按上面建议移除 rng，这里也需要同步调整函数签名与调用。

def run_epochs(N, qlmc, qtable, rng):

lectures/mccall_q.md:673

• 上面若移除了 run_one_epoch(..., rng) 的 rng 参数，这里也需要同步更新调用，否则会出现参数个数不匹配。

        new_qtable = qlmc_new.run_one_epoch(qtable, rng)

lectures/mccall_q.md:596

• 上面若移除了 run_epochs(..., rng) 的 rng 参数，这里的调用也需要相应更新，否则会出现参数个数不匹配。

qtable = run_epochs(20000, qlmc, qtable0, rng)

lectures/mccall_q.md:533

• 同上，run_one_epoch 作为 jitclass 方法目前依赖 rng.random() 并把 rng 传给 temp_diff / draw_offer_index。这会把 np.random.Generator 引入 numba 编译域，通常会直接编译失败。建议移除 rng 参数，并在方法内改回 np.random.random()。

    def run_one_epoch(self, qtable, rng, max_times=20000):
        """
        运行一个"轮次"。
        """

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