ResultReproduce the Klein-Friedmann Q-learning report in its own simulator at pinned commit 8cc8a0e

The shipped Klein-Friedmann code (github.com/ekreate/cs221-final-project @ 8cc8a0edfa04f1a93088c951e217d3cd3d6013f0) reproduces its report in its own simulator: uniform-random means 31.657, 31.733, 31.851 over 5,000 games each (report 31.2), and the Q-learner's 10,000-game test means 49.080, 49.053, 49.015 moves with standard deviations near 11.7 (report 49.61, sd 11.18) after 50,000 training games each, so both preregistered bands pass. Two of the theory's clause-(ii) checks were decided here. (ii-a) Front-loading holds: a learner stopped after 300 training games (13,796 updates) tests at 49.0796 moves against 49.0795 for the 50,000-game learner (2,406,211 updates) on the same 10,000 test games, a difference of -0.0001; the per-1,000-game training curve is already at 46.8 in its first block and 48.2 in its last. (ii-b) The report's regularisation claim does NOT hold for the shipped configuration: with lambda = 0 the learner tests at 48.967 moves, indistinguishable from the lambda = 0.1 arm and far above random, where the report's Figure 5 says the unregularised agent fell below random. With eta = 1/t counted per move, the ridge step eta*lambda*w is of order 1e-5 for almost all of training, so the term cannot act; the report's divergence must come from a schedule the shipped code no longer contains. Units are the upstream simulator's (one point per surviving move, 200-move cap, empty starting board, dropping on a full column ends the game); nothing here is a corrected-Hardcore score or a repository cohort.