Trials 3+4 both proposed ~140k steps and hit the 2hr JOB_TIMEOUT,
wasting time and producing no GP data. At ~20 steps/sec, 120k steps
takes ~100 min, safely within the 2hr limit.
Agent: pi
Tests: 102 passed
Tests-Added: 0
TypeScript: N/A
StuckTerminationWrapper added to wrap_env stack (between ThrottleClamp
and SpeedReward):
- Terminates episode after stuck_steps=80 steps with <0.5m displacement
- Handles slow barrier contact that Unity hit detection misses
- Handles off-lap-line circles (efficiency→0 gave zero reward but no
termination; now gives -1.0 after 80 steps = ~4s of non-progress)
- Wrapper stack: ThrottleClamp → StuckTermination → SpeedReward
Also: missing deque import in multitrack_runner.py caused NameError.
Phase 4 results cleared again (Trial 1 ran without StuckTermination).
Tests: 2 new stuck-termination tests, 102 total.
Agent: pi
Tests: 102 passed
Tests-Added: 2
TypeScript: N/A
Two reward hacking behaviours observed during Wave 4 training:
1. Short-lap circle exploit (reported by user, echoes Toni's guardrail hack):
Model circles at start/finish line completing laps in 1-2 sim-seconds,
accumulating lap_count indefinitely with no genuine track progress.
Fix: SpeedRewardWrapper detects lap_count increment; if last_lap_time
< min_lap_time (5.0s), returns penalty = -10 × (min_lap_time / lap_time).
A 1-second lap gives -50 penalty. Legitimate 12-second laps unaffected.
Window size also increased from 30 → 60 to catch slower circles.
2. Non-terminating segment eval episodes:
evaluate_policy on wide tracks (no barriers) could run indefinitely,
inflating segment_reward to 200k+. Replaced with manual eval loop
capped at MAX_EVAL_STEPS=3000 steps.
Phase 4 results cleared (trials 4-6 ran with exploitable reward).
Tests: 4 new reward wrapper tests, 100 total passing.
Agent: pi
Tests: 100 passed
Tests-Added: 4
TypeScript: N/A
Without this, Wave 4 scratch-trained models produce no rollout stats in
the log, making it impossible to monitor training progress or spot
degenerate policies early.
Warm-start models in Wave 3 showed stats because verbose=1 was baked
into the Phase-2 saved model state; fresh models default to verbose=0.
Agent: pi
Tests: 96 passed
Tests-Added: 0
TypeScript: N/A
Paul Huliganga