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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)

Exp 8 result: If best_model.zip drives mountain_track reliably → proceed to Step 2
Combine mountain_track + generated_track using v5 reward, throttle_min=0.5, proper checkpointing
Test combined model on all 4 tracks — can it generalise to mini_monaco like Trial 9 did?
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

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