# 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


### Exp 8 — Mountain track, v5 reward, throttle_min=0.5, CORRECT checkpointing ✅ COMPLETED
- **Reward:** v5 (speed × CTE-quality)
- **throttle_min:** 0.5
- **Method:** Direct model.learn() loop, single TCP connection, NO close_and_switch
- **Steps:** 90,000 total | 6,000 per segment | 15 checkpoints
- **Circle exploit fix:** Short-lap terminates episode immediately
- **Peak segment:** Seg 10 (step 60,000) — 567 reward / 2000 steps (FULL EVAL on mountain_track!)
- **Policy diverged:** Seg 11-15 (31, 20 reward) — best_model.zip captured the peak correctly
- **Checkpoints saved:** checkpoint_0006000.zip through checkpoint_0090000.zip + best_model.zip
- **Final eval results using best_model.zip (step 60k weights):**

| Track | Ep1 | Ep2 | Ep3 | Mean steps | Result |
|---|---|---|---|---|---|
| mountain_track (training) | 382 | 529 | 182 | 364 | ❌ crashes |
| generated_track (zero-shot) | 63 | 61 | 61 | 62 | ❌ crashes |
| mini_monaco (zero-shot) | 154 | 155 | 104 | 138 | ❌ crashes at one corner |
| generated_road (zero-shot) | 41 | 42 | 41 | 41 | ❌ crashes |

- **Throttle test:** mini_monaco at throttle_min=0.5 over 5 episodes: 93/94/79/95/94 steps (mean=91, very consistent = same corner every time). throttle_min=0.2 test impossible — action space baked in at training time.
- **Key findings:**
  1. ✅ Circle exploit fully eliminated — no short laps observed
  2. ✅ Best model saving worked — captured step 60k peak, not step 90k drift
  3. ✅ Genuine 20-22 second laps during training from step ~18k onward
  4. ❌ Model crashes at exactly the same corner on mini_monaco every time (too fast)
  5. ❌ throttle_min=0.5 baked into action space — model cannot output throttle < 0.5, cannot slow for corners
  6. 🔑 INSIGHT: v4 + 0.2 failed because v4 gradient = 0 on hill. v5 gradient is non-zero — model CAN learn to apply high throttle when needed even with 0.2 floor

### Exp 9 — Mountain track, v5 reward, throttle_min=0.2 (RUNNING)
- **Change from Exp8:** throttle_min: 0.5 → **0.2** (only change)
- **Reward:** v5 (speed × CTE-quality) — UNCHANGED
- **Hypothesis:** v5 reward provides non-zero gradient signal on hill (∂reward/∂speed is non-zero). 
  Model CAN learn to output high throttle on hill. With 0.2 floor, model has full range [0.2, 1.0]
  and can apply lower throttle on corners — potentially solving mini_monaco corner crash.
- **What we never tested:** (0.2, v4) failed. (0.5, v5) worked. (0.2, v5) was never tried.
- **Risk:** Model may still stall on hill if gradient convergence is slow in early training.
  StuckTermination (-1.0) + v5 speed gradient together should push toward higher throttle.
- **Next test (Exp10):** Add track_progress bonus to reward (v6) — one variable at a time.
- **Save dir:** models/exp9-mountain-v5-throttle02/
- **Watch:** tail -f /tmp/exp9.log


### Exp 9 — Evaluation Results (3-set test, 1 run per track per set)

**Model tested:** `models/exp9-mountain-v5-throttle02/best_model.zip`
**Date:** 2026-04-18
**Test setup:** 3 independent sets, lighting randomises each run (no fixed seed)

| Track | Set 1 | Set 2 | Set 3 | Mean | Pattern |
|---|---|---|---|---|---|
| mountain_track (trained) | ✅ 2000 | ✅ 2000 | ✅ 2000 | **2000** | Rock solid |
| generated_track (zero-shot) | ❌ 79 | ❌ 61 | ❌ 82 | **74** | Always fails — can't make first corner |
| generated_road (zero-shot) | ❌ 651 | ✅ 2000 | ❌ 1203 | **1285** | Highly variable — lighting dependent |
| mini_monaco (zero-shot) | ❌ 32 | ❌ 60 | ❌ 34 | **42** | Always fails — veers right immediately |

**User observations:**
- mountain_track: 80-90% of time on or near centre yellow line. Solid driving.
- generated_road: Driving looks good when it works, but goes off course. Lighting variation causes inconsistency.
- generated_track: Cannot make first corner at all. Model sees nothing it recognises.
- mini_monaco: Veers right immediately at start before any visible driving. Crashes before reaching the road.

**Key finding — Lighting effect confirmed:**
Generated_road varies 651→2000→1203 with identical model and track. ONLY lighting changes.
Mountain_track is immune because it trained under many random lighting conditions.
Generated_track and mini_monaco fail regardless of lighting — visual domain too different.

**What this tells us about next steps:**
Train on mountain_track + generated_track together (v5 reward, throttle_min=0.2).
Both tracks have random lighting each episode → model forced to learn lighting-invariant features.
Goal: model that is reliable on both training tracks, then test generalisation to generated_road and mini_monaco.


### Exp 10 — Two tracks: generated_track + mountain_track, v5 reward, throttle_min=0.2
- **Change from Exp9:** Added generated_track as second training track
- **Reward:** v5 (speed × CTE) — unchanged
- **throttle_min:** 0.2 — unchanged from Exp9
- **Training tracks:** generated_track + mountain_track (round-robin, switch every 6,000 steps)
- **Total steps:** 90,000 | Steps per switch: 6,000 | ~7.5 rotations through both tracks
- **lr:** 0.000725 — unchanged
- **Hypothesis:** Adding generated_track visual diversity forces model to learn
  lighting-invariant road-following features. Mountain_track teaches hill throttle.
  Together should generalise better to generated_road and potentially mini_monaco.
- **Expected results:** mountain_track reliable, generated_track reliable,
  generated_road improved, mini_monaco TBD
- **This is essentially Trial 9 repeated with:** v5 reward + throttle_min=0.2 + proper checkpointing + exploit fix

### Exp 10 — Evaluation Results (3-set test, 2026-04-19)

**Model tested:** `models/exp10-two-tracks/best_model.zip`
**Result: TOTAL FAILURE — crashes on every track, every set.**

| Track | Set 1 | Set 2 | Set 3 | Mean | Verdict |
|---|---|---|---|---|---|
| mountain_track (trained) | 178 | 179 | 179 | **179** | ❌ Crashes at same spot every time |
| generated_track (trained) | 99 | 82 | 88 | **90** | ❌ Crashes almost immediately |
| generated_road (zero-shot) | 135 | 223 | 105 | **154** | ❌ Crashes early |
| mini_monaco (zero-shot) | 111 | 133 | 129 | **124** | ❌ Crashes early |

**Comparison to previous best models:**
- Exp 9 (mountain only): mountain_track was 2000/2000 every time → now 179. **91% regression.**
- Wave 4 Trial 9 (generated_track + mountain_track via autoresearch): generated_track 2000/2000, mini_monaco 2000/2000 → now 90 and 124.

**Analysis:**
- The round-robin track switching every 6,000 steps via `multitrack_runner.train_multitrack()` 
  produced a model that learned NEITHER track. This is catastrophic interference.
- Wave 4 Trial 9 used the same two tracks but via the autoresearch controller with different
  hyperparameters (switch=6,851, lr=0.000725, 90k steps). The key difference is likely in
  HOW the environment switching works — `multitrack_runner` closes and reopens envs,
  potentially disrupting PPO's rollout buffer and value function estimates.
- Mountain_track crashes at exactly step 178-179 in all 3 sets — suggests the model has
  learned a fixed degenerate policy (always turn one direction) rather than responding to vision.

**Key question:** Why did Wave 4 Trial 9 succeed with similar parameters but Exp 10 failed?
  Possible causes: (1) env close/reopen resets PPO internal state, (2) `best_model` selection
  criteria differs, (3) multitrack_runner wrapping chain differs from autoresearch controller.

**Full log:** `agent/test-results/2026-04-19_10-15_exp10-two-tracks.log`

### Exp 9 vs Exp 10 — Root Cause Analysis

| Aspect | Exp 9 (worked ✅) | Exp 10 (failed ❌) |
|---|---|---|
| **Tracks** | mountain_track **only** | generated_track + mountain_track (round-robin) |
| **Env setup** | `VecTransposeImage(DummyVecEnv([make_env]))` — created ONCE, never closed | `wrap_env(raw)` passed to PPO, which auto-wraps; **closed and reopened** every 6k steps |
| **Track switching** | None — single env for entire 90k steps | `close_and_switch()` — close env, exit_scene, sleep, gym.make new track |
| **PPO continuity** | Single `model.learn()` calls with `reset_num_timesteps=False`, same env | `model.learn()` + `model.set_env(new_env)` after each switch |
| **Eval between segments** | Direct `env.reset()` + predict loop on same env | Same, but env may be a different track than what was just trained |
| **Best model selection** | Based on eval reward on mountain_track | Based on segment reward — could be from either track |

**Conclusion:** Exp 9 kept a single persistent env connection for all 90k steps.
Exp 10 closed and reopened the env every 6k steps with `model.set_env()`.
This likely disrupts PPO's rollout buffer, value estimates, and observation normalization.
Exp 9 was a completely different (simpler) script with no track switching at all.

### Exp 10 vs Wave 4 Trial 9 — Why Did Trial 9 Work?

Wave 4 Trial 9 used nearly identical hyperparameters to Exp 10 and the SAME
`multitrack_runner.py` code — yet Trial 9 scored 1435 on mini_monaco (zero-shot)
while Exp 10 crashes on every track at <180 steps.

**Wave 4 Trial 9 parameters:**
- lr=0.000725, steps_per_switch=6,851, total_timesteps=89,893
- Trained on generated_track + mountain_track (same as Exp 10)
- Used `multitrack_runner.py` via CLI subprocess (same close_and_switch logic)

**Exp 10 parameters:**
- lr=0.000725, steps_per_switch=6,000, total_timesteps=90,000
- Nearly identical to Trial 9

**But Wave 4 was mostly failures too:**

| Metric | Value |
|---|---|
| Total Wave 4 trials | 25 |
| Scores > 500 | 4 / 25 (16%) |
| Scores > 200 | 5 / 25 (20%) |
| Median score | 111.3 |
| Mean score | 343.8 |
| Std deviation | 566.2 |

The top 4 scores (1943, 1573, 1543, 1435) are massive outliers — 80% of trials
scored below 200. Trial 0 (score 1943) was later found to be pre-exploit-patch
and Trial 14 (1573) and Trial 25 (1543) showed inconsistent driving when
re-tested (see STATE.md).

**The real conclusion:** Trial 9's success was likely due to **lucky random
initialization of CNN weights**. With 80% of trials failing under the same
training methodology, the multitrack round-robin approach via close_and_switch
is fundamentally unreliable. The few successes are random seed lottery winners,
not evidence that the method works.

**Wave 5 reproduction attempt:** We tried training on generated_track only
(single track, no switching, same lr=0.000725, 90k steps) to test whether
the track-switching was the problem. Result stored in `models/wave5-gentrack-only/`.
(Results were poor — could not reproduce Trial 9's quality.)

**Open question:** Is there a reliable way to do multi-track training, or
should we focus on single-track training with domain randomization (lighting,
camera angle) to achieve generalization instead?