SirStone
b68707c867
The user's request: "when our bot is low OR enemy is low, it is useless to use high power instead low fast bullets have more chances to finish the enemy. Let's do a math slope: starting from some health down, the power goes down with it." 1. ENERGY SLOPE (`TR_POWER_ENERGY_*`), replacing the old hard step at 50 energy: cap = ENERGY_MAX at/above ENERGY_HI, ENERGY_MIN at/below ENERGY_LO, LINEAR in power between, clamped. Defaults HI=80 LO=20 MIN=0.5 MAX=3.0, so no cap >=80, 0.5 at <=20, and e.g. E=65 -> 2.375, E=50 -> 1.75, E=35 -> 1.125. Rationale: bullet speed is 20-3p, so lower power = FASTER bullet (less lead error, higher hit chance), fires more often (10+2p) and drains slower (p/shot). E[dE] = p(3P-1) => break-even hit probability is 1/3 INDEPENDENT of power, and our measured rates are 5-27%, far below it. 2. FINISHING CAP (`TR_POWER_FINISH_KILL`, default ON): cap power at the SMALLEST bullet that still removes the enemy's remaining energy - `E<=4 -> p=E/4` (min 0.1), `4<E<=16 -> p=(E+2)/6`, `E>16 -> no cap`. Rationale, and it makes the user's instinct stronger than a heuristic: server 1.3.1 caps the damage SCORE at the energy ACTUALLY REMOVED, so overkill is WASTED damage AND ~6x the energy for ZERO extra score. Damage is 4p (p<=1) / 6p-2 (p>1). Both are min-composed with the existing far/below-average caps, may only LOWER power (exhaustively tested), and are exempt while ramming. `TR_POWER_POLICY=0` still returns the uncapped control exactly. MEASURED ENERGY SAVING (offline replay of the DrussGT fixtures, 28,797 ticks): arm shots energy meanP E/1k ticks vs cliff control(uncapped) 1913 4646 2.43 161.4 -90.2% cliff (today) 2363 2443 1.03 84.8 0.0% slope 2404 2178 0.91 75.6 ** 10.9% LESS ** slope+finish 2404 2167 0.90 75.3 ** 11.3% LESS ** So the slope spends ~11% less energy than the cliff AND fires slightly MORE shots (2404 vs 2363) - both directions at once. HONEST NOTE on the finishing rule's reach here: ticks where the enemy is low (0 < E <= 16) are only 2252/28797 = 7.8% of these fixtures, so finishing adds just ~11 energy of saving against DrussGT. It matters in CLOSER fights, not this one. Verification: test_power_policy 58 (was 26) in BOTH the default and TR_POWER_POLICY=0 control arms - slope at E=100/80/65/50/35/20/5, powerToKill across E=0.1..100, the inverse-cover property for E<=16, monotonicity, ram exemption, and an exhaustive sweep proving power <= preference. Guards: test_gun_harness 39, test_vbullet_metric 11, test_power_selection 3, test_adaptive_radar 41, test_tfil_ring_weights 24, test_ram_decision 40, test_rack_membership 48, test_selector_tiebreak 19, test_tm_pattern_registration 20, test_vbullet_admit_gate 12. acceptance 12/12 PASS. ModularBot compiles release. Adds `common_libs/tests/measure_power_policy.nim` (the energy/histogram tool) and updates docs/env_reference.md for the new `energySlope|finishKill` log reasons. NOT MEASURED: the battle/hit-rate effect. The offline figures use the fixture shooter's energy as a proxy, open-loop; the RELATIVE saving is the meaningful part.
54 lines
1.1 KiB
Plaintext
54 lines
1.1 KiB
Plaintext
# Compiled output
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*.out
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# Python cache
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__pycache__/
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*.pyc
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*.pyo
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# Log files
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*.log
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# Training logs
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*.jsonl
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# Test binaries (but keep .nim source files)
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**/tests/test_*
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!**/tests/test_*.nim
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!**/tests/test_*.nims
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# User-specific dev environment
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.envrc
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devbox.json
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devbox.lock
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# Compiled bot binaries — all builds go to *_garage/out/
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*_garage/out/
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# Java compiled classes
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*.class
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# Log and snapshot directories
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**/logs/
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**/snapshots/
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# Git worktrees
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worktrees/
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# Nimble puts the bot binary at the garage root (out/ is ignored, this was not)
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*_garage/ModularBot
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# Test fixtures are data, not logs - the *.jsonl rule above was written for
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# training logs and silently excluded the entire gun-range fixture set.
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!tools/fixtures/**/*.jsonl
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# Compiled test harnesses have no extension; the test_* rule misses them
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common_libs/tests/tm_measure
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common_libs/tests/run_range
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common_libs/tests/gen_synthetic_fixtures
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common_libs/tests/acceptance_offline_vs_online
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common_libs/tests/measure_power_policy
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common_libs/tests/test_tsetlin_gun
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common_libs/tests/test_tsetlin_live
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common_libs/tests/test_tm_pattern_learning
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