35 lines
2.2 KiB
Plaintext
35 lines
2.2 KiB
Plaintext
# ─────────────────────────────────────────────────────────────────────────────
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# arms_movement_learned.txt — the LEARNED-MOVEMENT arms (state-conditional
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# counted-SBC wave danger), on the FROZEN panel tools/ab/panel_movement.txt.
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#
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# Pre-registered in docs/movement_campaign.md, "Learned movement (SBC)", BEFORE
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# any battle. REFERENCE is `strafe` — the SHIPPED champion. Every delta is
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# (arm − strafe).
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#
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# Offline gate (common_libs/tests/learned_surfer_gate.py) says: the
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# state-conditional model beats the global histogram and chance on held-out
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# log-loss in 63/63 battles (sign-flip p = 5e-5) but the effect is TINY
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# (top-1 3.93% vs global 3.96% vs chance 3.23%). So the live panel decides.
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#
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# Format: name | ENV=value ENV=value | label
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# ─────────────────────────────────────────────────────────────────────────────
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# 1. THE CHAMPION — the arm a challenger has to beat (round wins + hit rate).
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strafe | TR_MOVEMENT=strafe | champion/reference — shipped strafe defaults
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# 2. THE MODULE — state-conditional counted SBC, decay 128 learns / shift 1.
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learned | TR_MOVEMENT=learned | state-conditional learned danger (decay on)
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# 3. FORGETTING CONTROL — the same learner with decay disabled: isolates the
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# counted+decay memory mechanism. (Offline this arm is the BEST top-1 arm,
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# which is itself a prediction: the corpus is stationary, the live opponent
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# is not.)
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learned_nodecay | TR_MOVEMENT=learned TR_LEARNED_DECAY_SHIFT=0 | same, no forgetting
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# 4. INFORMATION CONTROL — the same mover, the same geometry, the same counted
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# SBC, but the state is forced to a single cell, i.e. the OLD global
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# histogram. This separates "the learned state conditioning" from "the wave
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# geometry + counted prior" and is the arm that answers "is the failure in
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# the information or in the learner?".
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learned_global | TR_MOVEMENT=learned TR_LEARNED_GLOBAL=1 | same mover, state conditioning OFF
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