Dense firing (~50%) caused all readout bins to saturate identically.
Replace threshold-based firing with k-WTA: only top 50 neurons fire
per tick (~5% sparsity). Sparse patterns give low inter-angle overlap
-> readout bins can discriminate between different bearings.
- Threshold 4→1: neurons now fire (~50% rate) instead of never firing
- Remove aggressive decay that erased learning signal immediately
- Remove EVALUATE re-forward that corrupted reservoir state before learn()
New architecture: 1024 binary neurons in fixed random reservoir,
72-bin population-coded output, WTA Hebbian learning with binary ops.
Forward pass: AND + popcount. Learning: OR (reinforce) / AND NOT (punish).
No backprop, no floats in hot path. Toggle via USE_RESERVOIR const.
Forecast: ~200-400 ticks to learn stationary target aiming.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Delete ring buffer, aim snapshots, maturation loop (~80 lines).
Replace with simple velocity extrapolation: predict enemy position at
bullet impact time, use bearing to predicted position as SuperSpike
target. Immediate error signal every EVALUATE — no delay, no snapshot
state storage. Mathematically equivalent to retroactive for linear movers.
Fire during WAITING phase using FIRE_POWER constant (matches error
signal's bullet speed calculation). Always shooting — fire timing
optimization is out of scope for this map.
Multiple superSpikeUpdate calls per EVALUATE caused oscillation by
effectively multiplying the learning rate. Now: accumulate retroTarget
via circular mean across all matured snapshots, apply one update with
the averaged target and most recent snapshot's SNN state.
Multiple snapshots can mature by EVALUATE time but only the last one
triggered a superSpikeUpdate. Earlier matured snapshots were silently
discarded — wasting learning signal. Now each matured snapshot with a
valid ring buffer hit gets its own weight update.
Weights were barely moving: |wih| +0.14%, |wOut| +7.7% over full session.
Bump output ETA 2x and input→hidden ETA 4x to accelerate convergence.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Lower THRESH 0.2→0.08 to increase hidden firing rates (output layer was frozen due to rate≈0 in weight updates)
- Add ETA_IH=0.005 for input→hidden updates (10x smaller than output ETA=0.05 to prevent weight thrashing from large preTrace magnitudes)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The retroactive error signal matures many ticks after the snapshot was taken,
but superSpikeUpdate was using bot.lastSpikes/lastVSnap which had been
overwritten by subsequent DECIDE cycles. Learning was applied against wrong
neural activity — effectively random weight perturbations.
Fix: AimSnapshot now captures spikes, vSnap, and preTrace at DECIDE time.
superSpikeUpdate receives these saved values at maturation instead of the
stale current state.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Expand input layer from 36 to 80 neurons: bearing (0-35) + velocity direction (36-71) + speed (72-79)
- Population-code velocity direction (same 10° band scheme as bearing) and speed (8 bands, 1 unit/tick)
- Compute 1-tick velocity from position deltas in onScannedBot
- Add 100-slot ring buffer with per-slot tick tracking for enemy positions
- Replace instantaneous bearing error with retroactive would-have-hit signal
- Skip learning until first aim snapshot matures (buffer fill period)
- Delayed-target SuperSpike: weight updates use most recently matured retroactive bearing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Document exception handling, zero-value BotResult trap, shared adversary bots
- Add offline parsing example using parseServerOutput
- Skip tests gracefully when JARs missing (guard before suite blocks)
- Fix blocking readLine in runner_process.nim: poll with 50ms sleep + atEnd check
(was preventing timeout enforcement, now blocks correctly during battle)
- Add test task to QBot.nimble and config.nims setup docs to AGENTS.md
- Add debug logging to TestBattleRunner for bot identity tracking
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implements:
- BattleResult type and JSON-lines parser (#127)
- TR server lifecycle manager (#128)
- Bot compiler using nim c (#129)
- runBattle() orchestrator (#130)
- Example test in OscillatorBot_garage (#131)
- Framework usage guide (#132)
- TestBattleRunner.java for external server (#133)
- BattleRunner process lifecycle (#134)
Fix: runner_process.nim was redefining TimeoutError locally; now
uses std/net.TimeoutError consistently with server_manager.nim.
- Deleted constants.nim, event_queue.nim, graphics.nim, json_parse.nim, schemas.nim, utils.nim, and ws_client.nim files.
- This cleanup removes unused code and simplifies the codebase, focusing on essential functionalities.
- Removed non-existent exported/ directory from layout docs
- Replaced hardcoded bot binary entries with *_garage/out/ pattern for build directories
- Added all known bot binaries (GotoTest, OscillatorBot, PPO_Bot, QBot, SAC_LSTM_Bot)
to .gitignore with clarifying comment about Nim's compilation target structure
Nim places compiled binaries at bot root (no extension) and in out/ subdirs.
The out/ pattern catches all build outputs; specific bot binaries listed for
root-level executables since gitignore lacks a reliable "no-extension files" glob.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Extracted concrete numbers from 13 papers in docs/papers/neuroevolution/.
Key findings: use CMA-ES or mutation-only truncation GA, mutate ALL weights
(not 5%), sigma=0.005-0.01, pop=64-200, no crossover, single elite.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
SirStone