Hard has been a label-only duplicate of medium.json since medium was promoted
on 2026-08-17. Promote 20260823-1734-gen5-s5-intercepts-retry2 into
hard.json so the tier is a genuinely distinct policy, and so the strongest bot
the curriculum has produced survives the next round's checkpoint pruning —
promoted files are never touched by training scripts.
Stage 5 blocked after three attempts, so like medium.json this comes from a run
recorded as decision: "fail". Both failing floors are covered in TRAINING.md:
goal_rate 0.7369 vs 0.75 is marginal, and productive_air_touch_fraction 0.0001
vs 0.005 is a bar no policy in the lineage has approached, against a metric
quantised at 0.01 per ~100-episode window. On every other axis it is the best
yet: upright_fraction 0.757 against a 0.40 floor that the pre-Round-6 lineage
never pushed past 0.331, and forward_motion_fraction 0.479 against 0.20.
Chosen over attempt 2 (retry1) on a tiebreak, not a margin. retry1 posts a much
wider indirect result against medium.json (63-23-14 vs 47-32-21), but a direct
100-episode head-to-head between the two finished 36-39 with 25 draws, so that
gap does not reflect a real strength difference. Attempt 3 is the later
checkpoint (it resumed from attempt 2) and edges every telemetry metric.
Verified: hard.json is byte-identical to its source export, matches easy/medium
on input_size 83, 3 layers and action space, and beats medium.json 19-7-4 in a
fresh 30-episode paired run. Tiers stay monotonic: hard > medium > easy.
That head-to-head also showed a 17% physical side imbalance (physical teams
0-1 = 29-46), reproduced at 13% in the 30-episode check. Inside the 20% bar
used elsewhere and equal across both models, but noted in TRAINING.md as worth
investigating rather than assuming variance.
air_approach_weight alone didn't move productive_air_touch_fraction after a
further 180M steps (360M cumulative across all six Stage-5 attempts): an
unredirected air-intercept ball falls short of the goal from gravity and
just lands on the floor, so the already-solved ground game collects the
same episode reward whether or not anything touched the ball in the air.
air_touch_bonus_weight adds a conjunctive event bonus on top of
ball_touch_reward for a touch that's both genuinely aerial and
goal-directed, targeting the actual measured behaviour instead of only the
approach to it.
Stage 5 blocked all three attempts on productive_air_touch_fraction
stuck exactly at 0.0 across a continuous 180M-step lineage, while
goal_rate/upright_fraction/forward_motion_fraction kept improving on
the same budget. forward_velocity_to_ball_weight (the term that solved
Stage 4's ground pursuit) is hard-gated below GROUND_HANDLING_HEIGHT
and does nothing in the air, so Stage 5's air_intercept_chance had no
matching aerial incentive to learn from. air_approach_weight adds the
airborne mirror (nose-first 3D closing speed, no uprightness
multiplier) and folds into HANDLING_REWARD_FLAGS so Stage 6 inherits
it too. Deleted the three blocked attempts and reset state to resume
Stage 5 from the Stage-4 checkpoint with the new term.
20260816-2126-gen5-s4-handling-retry2 exhausted its three attempts and
missed only the 0.80 training goal-rate floor, at 0.7731. Every
evaluation gate passed: 65-22-13 versus promoted/easy.json, 87% non-draw
against an 80% floor, 12.6% physical-side imbalance against a 20%
ceiling, and both handling telemetry floors clear. The round improved the
goal rate monotonically across attempts (0.537 -> 0.683 -> 0.773) and the
checkpoint plays well by hand, so close Stage 4 by human override.
Promote it to Game/bots/promoted/medium.json. Medium and Hard both point
at the new policy: Hard stays a label-only duplicate until a stronger one
earns hard.json, which keeps the tiers monotonic rather than leaving Hard
weaker than Medium.
generation5_state.json flips that log entry to "pass" with a
decision_override block preserving the original verdict and reasoning,
and advances to Stage 5 attempt 1. This is what passing_entry() needs to
resolve Stage 5's resume checkpoint and evaluation reference, and what
league_pool() will need at Stage 6; --skip-to-next-stage would advance
the stage without marking anything as passing and die immediately.
generation5.sh now pulls before launching. Each stage ends in
commit_progress()'s push, which fails and kills the run hours in if the
box is behind origin.
Six rounds of reward shaping (~700M steps) failed to produce upright ground
driving. A critical review of the simulation rather than the reward found
why:
1. The hull was a 1x1x4 box with inertia (1,1,1) and no restoring torque
anywhere, so belly-down and rolled-90 were geometrically identical
resting states. "Upright" was not a physically distinguished state at
all - the reward was paying for a property the simulation did not have.
2. ~65% of episodes spawned ships via _random_position, which samples Y
uniformly over the full 18m volume (mean ~8.7m). The measured
airborne_fraction ~0.44 was largely that spawn distribution, and every
ground-handling term fades out above 3m, so the shaping being tuned
barely ever applied.
3. air_drill_chance 0.20 spawned deliberately unreachable-without-climbing
states in the stage meant to teach ground driving, and its own
air_touch_fraction (0.0002) shows the drills were never solved.
Fixes land in the physics and the task distribution, not the reward:
- ship.tscn: hull 1x1x4 -> 1.6x0.6x4 so it has one stable resting face;
inertia (1,1,1) -> (7,1,7), physically correct for the hull, making
tumbling reluctant while keeping yaw snappy.
- ship.gd: new altitude-faded righting torque (spring-damper toward
belly-down, faded out by 3m so aerials keep full attitude freedom).
This is the grav-plating analogue of Rocket League's auto-righting and
helps human pilots land cleanly too.
- training_mode.gd: new ground_start_chance branch spawning ships level and
resting on the floor with a floor-level ball - the state the handling
stage's rewards are actually written for.
- generation5.py: ground-start-chance 0.50, air-drill-chance 0.20 -> 0.0.
Reward terms are left exactly as they were; they should finally pull in a
direction the ship can go.
Round 4 changed three things at once and two of them cut upright pressure:
grounded_upright_reward went to 0 and ground_tilt_penalty was cut 2.5x,
while the new uprightness multiplier only pays below GROUND_HANDLING_HEIGHT
*and* while moving forward *and* facing the ball - a far narrower slice of
ticks than the penalty it was meant to replace. Net pressure fell and
upright_fraction fell with it (0.268 -> 0.239 -> 0.238, the lowest of any
round). Restore ground_tilt_penalty to 0.05 and change nothing else, so
this is a genuine single-variable test of multiplier plus full tilt
pressure.
The conjunctive mechanism itself held up: forward_motion_fraction reached
its best sustained value (0.242) without goal_rate sagging, ep_rew_mean
turned positive for the first time (+0.28), and eval win rate hit 49% with
no reward hacking.
Also adds grounded_upright_fraction: a diagnostic, deliberately ungated
metric measuring uprightness over real floor-contact ticks instead of
sub-3m ticks. upright_fraction has never exceeded 0.331 across four rounds
and ~560M steps without cheating, and its denominator is dominated by
ballistic transit (airborne_fraction ~0.45, mean_altitude ~4.4m) where
attitude is not meaningfully controllable - so it likely cannot measure
what the 0.45 floor was meant to capture. Re-baseline that floor from what
this reports rather than from another round of reshaping.
Rounds 2 and 3 showed that tuning grounded_upright_reward's magnitude only
slides along a tradeoff instead of resolving it: at 0.015 upright_fraction
climbed to 0.331 while goal_rate sagged to 0.542 (then farmed outright at
0.696/0.366), and at 0.004 goal_rate climbed 0.569->0.604 while
upright_fraction went flat at ~0.26. An additive uprightness bonus is an
alternative to playing well, so the policy just picks whichever is cheaper
and no magnitude buys both behaviours.
Change the mechanism rather than the number: grounded_upright_reward drops
to 0, and uprightness becomes a multiplier inside the nose-led approach
term, which already requires moving forward at the ball. Parked-and-upright
and fast-but-sideways now both pay zero; only upright, forward, nose-on to
the ball pays full. forward-velocity-to-ball rises 0.06 -> 0.15 to offset
the ~2-3x expected-value cut from the new factor, and ground-tilt-penalty
drops 0.05 -> 0.02 now that uprightness is paid positively during play.
Delete the three blocked attempts and reset state to restart from the
Stage-3 foundation.
grounded_upright_reward at 0.015 overshot: four force-retries pushed
upright_fraction from 0.265 to a plateauing 0.331, then the fifth jumped it
to 0.696 (55% over the 0.45 floor) while goal_rate collapsed 0.542->0.366
and forward_motion_fraction fell 0.244->0.184 (vertical_thrust_mean went
negative) - the policy learned to sit pinned upright and farm the bonus
instead of chasing the ball. It was sized "comparable to
time_penalty/ball_distance_penalty" but at 0.015/tick it was actually above
ball_distance_penalty's 0.01/tick worst case, so idling near the ball beat
playing. Cut to 0.004/tick (episode ceiling ~7.2, below
ball_distance_penalty's ~18 worst case). Delete the five blocked attempts
and reset generation5_state.json so the next run starts fresh from the
Stage-3 foundation rather than continuing from the farming checkpoint.
Stage 4's upright/forward-motion telemetry plateaued flat across all three
blocked attempts because ground_tilt_penalty (0.003) was too weak to matter
and nothing penalized sideways/reverse motion at all. Raise
ground_tilt_penalty to 0.05 and add a new non_forward_penalty term
(ship_ai_controller.gd) that directly costs non-forward planar velocity near
the floor, independent of the ball. Delete the three blocked attempts'
checkpoints/logs/exports and reset generation5_state.json so the next run
starts fresh from the Stage-3 foundation checkpoint instead of continuing
from the drifted retry2 weights.
CosmicClash Training Bot