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+# Test History — DonkeyCar RL Autoresearch
+
+Last updated: 2026-04-18
+
+This document records every significant training experiment, what was
+changed, what was observed, and what was learned.  Use this to make
+methodical decisions rather than random changes.
+
+---
+
+## Baseline Models (Phase 1 & 2)
+
+### Phase 2 Champion
+- **Model:** `models/champion/model.zip`
+- **Track trained on:** generated_road only
+- **Steps:** 13,328
+- **Hyperparams:** lr=0.000225, PPO continuous actions, ThrottleClamp(0.2), v4 reward
+- **Result:** ✅ Drives generated_road perfectly, stays in right lane
+- **Zero-shot:** ❌ Fails on generated_track (confirmed), ❌ Fails on mini_monaco
+- **Notes:** Single track, simple road, model converged cleanly. Final model = best model (no divergence in 13k steps)
+
+---
+
+## Mountain Track Experiments
+
+All experiments: mountain_track only, lr=0.000725, throttle_min varies, 90k steps
+
+### Exp 1 — Mountain track, old v4 reward, throttle_min=0.2
+- **Reward:** v4 (CTE × efficiency × speed)
+- **throttle_min:** 0.2
+- **Key observation:** Car gets partway up hill, slows, stops, rolls back. Always crashes at same step (~153-166). Steps logged: 0.200 throttle at hill = not enough power
+- **Root cause:** v4 reward gives zero gradient signal on hill (efficiency→0, speed→0, reward→0 simultaneously, no direction for "apply more throttle")
+- **Learned:** v4 reward is broken for inclined terrain
+
+### Exp 2 — Mountain track, old v4 reward, throttle_min=0.2, continued to 200k
+- **Reward:** v4
+- **throttle_min:** 0.2
+- **Key observation:** Only 2 behaviors: turn left and hit barrier, or go straight and hit barrier at turn
+- **Result:** ❌ Killed early — no improvement
+- **Learned:** More steps alone cannot fix a broken reward signal
+
+### Exp 3 — Mountain track, old v4 reward, throttle_min=0.5
+- **Reward:** v4
+- **throttle_min:** 0.5 (increased to overcome hill)
+- **Key observation:** Circle exploit dominated entire run — 0.5-1.75 second laps throughout
+- **Lap times logged:** All short (exploit)
+- **Result:** ❌ Model useless (reward=4.99 after 90k steps)
+- **Learned:** Higher throttle got car over hill but circle exploit took over because v4 has no efficiency penalty when throttle is high
+
+### Exp 4 — Continued from Exp 3 (200k total), old v4 reward, throttle_min=0.5
+- **Reward:** v4
+- **throttle_min:** 0.5
+- **Key observation:** Killed early — same 2 behaviors (left into barrier, straight into barrier)
+- **Result:** ❌ Killed
+- **Learned:** Continuing bad training does not help
+
+### Exp 5 — Mountain track, v5 reward, throttle_min=0.5 ⭐ KEY EXPERIMENT
+- **Reward:** v5 (speed × CTE-quality) — NEW reward that directly incentivises throttle on hills
+- **throttle_min:** 0.5
+- **Method:** Direct model.learn() — NO train_multitrack(), ONE connection throughout
+- **Key observation:** Genuine 20-22 second laps appearing from step ~30,000 onward
+- **Lap times:** 19-22 seconds (genuine), consistently for 60k steps
+- **Result:** ❌ Final model poor — best model was at step ~30k but we only saved final (step 90k) model
+- **Root cause of failure:** No best-model saving. Policy peaked at 30k, diverged by 90k
+- **Learned:**
+  1. v5 reward WORKS for mountain track
+  2. throttle_min=0.5 WORKS for hill
+  3. Direct model.learn() (no track switching) avoids phantom car issues
+  4. MUST save best model during training, not just final
+
+### Exp 6 — Mountain track, v5 reward, throttle_min=0.5, train_multitrack (1 segment)
+- **Reward:** v5
+- **throttle_min:** 0.5 (first segment only — close_and_switch used 0.2 for subsequent segments)
+- **Method:** train_multitrack() with steps_per_switch=90000 (one giant segment = one checkpoint)
+- **Key observation:** Circle exploit dominated — only 0.5-1.75 second laps throughout
+- **Result:** ❌ Only 1 checkpoint saved (at step 90k). Best reward=4.99
+- **Root cause:** Using steps_per_switch=TOTAL_STEPS defeated checkpointing (one segment = one save). Circle exploit reappeared (different from Exp5 — random seed variation)
+- **Learned:** steps_per_switch=TOTAL_STEPS is WRONG for single-track training with checkpointing
+
+### Exp 7 — Mountain track, v5 reward + episode termination on short lap, throttle_min mixed
+- **Reward:** v5 + short-lap now TERMINATES episode (not just penalty)
+- **throttle_min:** 0.5 initial, 0.2 after segment 1 (bug: close_and_switch used module default)
+- **Method:** train_multitrack() with steps_per_switch=6000 (15 segments)
+- **Key observation:** Car in LEFT lane, sitting doing nothing. Not normal spawn position.
+- **Hypothesis:** Phantom car from Exp6's ghost car still in sim. Two TCP connections spawned two cars. User watched phantom (left lane, no commands). Training went to different car.
+- **Result:** ❌ Killed — phantom car issue
+- **Learned:**
+  1. close_and_switch() between segments creates phantom car risk for single-track training
+  2. throttle_min MUST be passed consistently — module default is 0.2, not 0.5
+  3. For single-track training: do NOT use close_and_switch() at all
+
+### Exp 8 — Mountain track, v5 reward + episode termination, throttle_min=0.5 consistently (RUNNING NOW)
+- **Reward:** v5 + short-lap terminates episode
+- **throttle_min:** 0.5 throughout (no close_and_switch = no module default override)
+- **Method:** Direct model.learn() in loop — ONE connection throughout entire run
+- **Checkpoints:** 15 numbered saves (every 6,000 steps) + best_model.zip
+- **PID:** 2941877, log: /tmp/exp8.log
+- **Status:** Running since 11:17, ~1h45m total
+- **Watch:** `tail -f /tmp/exp8.log`
+- **Success criteria:** Genuine 19-22 second laps appearing during training AND best_model.zip drives cleanly in deterministic eval
+
+---
+
+## Wave 4 Multi-Track Experiments (generated_track + mountain_track)
+
+### Trial 9 ⭐ BEST OVERALL MODEL
+- **Model:** `models/wave4-trial-0009/model.zip`
+- **Tracks:** generated_track + mountain_track (round-robin, switch every 6,851 steps)
+- **Steps:** 89,893 total (~45k per track)
+- **Hyperparams:** lr=0.000725, switch=6,851
+- **Reward:** v4 (old — before exploit patches)
+- **Result:**
+  - ✅ Drives generated_track (3/3 episodes, 13-16 second genuine laps)
+  - ✅ Drives mini_monaco zero-shot (2000 steps, 40-second genuine laps — never seen in training)
+  - ❌ Crashes on mountain_track (~200 steps — hill + corner)
+  - ❌ Crashes on generated_road (~46 steps — turns right immediately)
+- **Notes:** Only 1 of 25 Wave 4 trials succeeded. Suspected random seed luck. Same hyperparameters repeated in Exp2 (overnight) produced useless model.
+
+### Wave 4 Other Trials (1-25 except Trial 9)
+- **Result:** All crashed on mini_monaco within 20-265 steps
+- **Median mini_monaco score:** ~112 (crashes at ~130 steps)
+- **Trials 14, 25:** Scored 1573, 1543 — suspected shuttle exploit (car going back and forth on straight)
+- **Learned:** Multi-track training is highly sensitive to random seed. GP+UCB did not converge reliably.
+
+---
+
+## Key Decisions Made (What We Keep)
+
+| Decision | Reason |
+|---|---|
+| v5 reward: `speed × CTE-quality` | Directly incentivises throttle on hills. v4 gave zero gradient on inclines. |
+| throttle_min=0.5 for mountain_track | Overcomes hill. Car can now reach first corner. |
+| Short-lap penalty + EPISODE TERMINATION | Penalty alone insufficient — model stayed alive and accumulated rewards between laps. Termination makes circling strictly unprofitable. |
+| Numbered checkpoints every segment | Never lose a good mid-training model again (ADR-017) |
+| best_model.zip updated on new best segment score | Final model ≠ best model. Peak can be at 30k even if final is at 90k. |
+| Single TCP connection for single-track training | Avoids phantom car problem from close_and_switch() |
+| lr=0.000725 | From Trial 9 (best model). Consistent with good results. |
+
+## Key Problems Still Open
+
+| Problem | Status |
+|---|---|
+| Mountain track circle exploit | Partially fixed — episode termination added. Exp8 will show if it holds. |
+| Mountain track — car can't navigate first corner reliably | Still being investigated. Exp5 showed genuine laps so it IS solvable. |
+| Multi-track generalization is random-seed dependent | No reliable solution yet. Trial 9 was lucky. |
+| Mountain track model doesn't generalise to other tracks | Expected — single track training generalises poorly. Next step after Exp8 succeeds. |
+
+---
+
+## Next Steps (Proposed, Not Yet Run)
+
+1. **Exp 8 result:** If best_model.zip drives mountain_track reliably → proceed to Step 2
+2. **Combine mountain_track + generated_track** using v5 reward, throttle_min=0.5, proper checkpointing
+3. **Test combined model** on all 4 tracks — can it generalise to mini_monaco like Trial 9 did?
+4. **If yes:** We have reproduced Trial 9 reliably with a better reward function
+