mirror of
https://github.com/jcreek/CosmicClash.git
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Josh Creek
88591e031f
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.
549 lines
25 KiB
Python
549 lines
25 KiB
Python
"""Run the post-generation-4 curriculum from the promoted Stage-3 policy.
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This is intentionally separate from curriculum.py/curriculum_state.json:
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generation 4 is a completed lineage and its final checkpoint is generation
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5's fixed foundation. Stages 4-6 add one difficulty at a time:
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4 handling -- upright, nose-led low-altitude movement
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5 intercepts -- useful moving-ball aerial interceptions
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6 league -- robustness against a pool of frozen historical styles
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Each stage resumes from its passing predecessor, exports through the normal
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run_training.sh parity check, records tail telemetry, and runs a paired
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100-episode regression evaluation. State is restart-safe in
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generation5_state.json.
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"""
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from __future__ import annotations
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import argparse
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import json
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import pathlib
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import subprocess
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import sys
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from datetime import datetime
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from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
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TRAINING_DIR = pathlib.Path(__file__).resolve().parent
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REPO_ROOT = TRAINING_DIR.parent
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STATE_PATH = TRAINING_DIR / "generation5_state.json"
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EVAL_HISTORY_PATH = TRAINING_DIR / "eval_history.json"
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FOUNDATION_EXPERIMENT = "20260806-1939-curric-s3-gauntlet"
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FOUNDATION_CHECKPOINT = TRAINING_DIR / "checkpoints" / FOUNDATION_EXPERIMENT / "final.zip"
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FOUNDATION_EXPORT = REPO_ROOT / "Game" / "bots" / f"{FOUNDATION_EXPERIMENT}.json"
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PROMOTED_EASY = REPO_ROOT / "Game" / "bots" / "promoted" / "easy.json"
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MAX_RETRIES = 2
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EVAL_EPISODES = 100
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REGRESSION_MARGIN = 0.15
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STANDING_ARGS = ["--ent-coef", "0.01", "--entropy-floor"]
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# Scoring/ball-direction shaping inherited from generation 4. Handling
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# replaces half the orientation-agnostic closing reward and all generic speed
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# reward with nose-led ground approach, while keeping global tilt pressure
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# small enough for flight. The first three Stage-4 attempts (2026-08-08/09)
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# plateaued with upright_fraction/forward_motion_fraction flat at ~0.22-0.26
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# against 0.45/0.25 floors for 120M cumulative timesteps: ground_tilt_penalty
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# at 0.003 only cost a fully-sideways episode ~2.7 reward, trivial next to a
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# goal (80) or a touch (0.7). ground_tilt_penalty is raised ~17x to 0.05 (a
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# full sideways episode now costs ~45, comparable to a goal) and
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# non_forward_penalty is a new term (ship_ai_controller.gd) directly costing
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# sideways/reverse planar velocity near the floor, independent of the ball,
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# since nothing previously penalized that at all. Both are floor-proximity
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# penalties only, with nothing equivalent above GROUND_HANDLING_HEIGHT — on
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# its own that risks teaching "avoid the floor" instead of "handle well on
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# it", worsening Stage 3's already-airborne-heavy baseline. grounded_upright_
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# reward is the positive counterpart: a bonus for genuine floor contact
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# (not just low altitude) while upright, so grounding well is the locally
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# profitable choice rather than merely the least-punished one.
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#
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# Round 2 (2026-08-11): grounded_upright_reward at 0.015 overshot. Four
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# force-retries pushed upright_fraction from 0.265 to a plateauing 0.331,
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# then the fifth jumped it to 0.696 (55% over the 0.45 floor) while
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# goal_rate collapsed 0.542->0.366 and forward_motion_fraction fell
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# 0.244->0.184 — the ship learned to sit pinned upright on the floor
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# (vertical_thrust_mean went negative) and farm the bonus instead of
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# playing. Root cause: 0.015/tick was actually *larger* than
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# ball_distance_penalty's worst case (0.01/tick), so idling near the ball
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# beat chasing it — not "comparable to time_penalty/ball_distance_penalty"
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# as originally sized. Cut to 0.004/tick (a full grounded episode now caps
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# at ~7.2, versus ball_distance_penalty's worst-case ~18 and a single goal's
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# 80) — enough to stop "avoid the floor" without being worth farming over
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# actually playing. Resets from the Stage-3 foundation again rather than
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# continuing from the farming checkpoint, same reasoning as the ground_tilt/
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# non_forward_penalty retune: don't resume a policy shaped by one reward
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# balance into a meaningfully different one.
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#
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# Round 3 (2026-08-12): 0.004 stopped the farming (vertical_thrust_mean
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# stayed positive, airborne_fraction flat) and goal_rate rose across the
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# chain 0.569->0.598->0.604 — but upright_fraction went flat at ~0.26, and
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# retry1 posted the best head-to-head in Stage-4 history (eval goal_rate
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# 0.820, 53-29-18). Lining rounds 2 and 3 up by attempt shows the actual
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# problem: where upright climbed goal_rate sagged, and where goal_rate
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# climbed upright went flat. An *additive* uprightness bonus is an
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# alternative to playing well, so the policy just picks whichever is
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# cheaper and the magnitude only slides along that tradeoff — no value can
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# buy both. Round 4 therefore changes the mechanism instead of the number:
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# grounded_upright_reward drops to 0, and uprightness becomes a multiplier
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# inside the nose-led approach term (ship_ai_controller.gd), which already
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# requires moving forward at the ball. Upright now pays only *while*
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# playing, so parked-and-upright and fast-but-sideways both pay zero and
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# only all three behaviours together pay full.
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#
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# forward-velocity-to-ball rises 0.06 -> 0.15 because multiplying by
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# uprightness cuts that term's expected per-tick value roughly 2-3x at
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# current behaviour; without the raise the approach incentive would quietly
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# weaken. non-forward-penalty is unchanged at 0.04 — it targets a specific
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# behaviour and has not misfired.
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#
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# Round 5 (2026-08-14): round 4 also dropped ground-tilt-penalty 0.05 ->
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# 0.02 on the theory that the multiplier could carry posture on its own.
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# That confounded the experiment — two of the three changes *reduced*
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# upright pressure at once (grounded_upright_reward to 0, tilt penalty cut
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# 2.5x) while the multiplier only pays below GROUND_HANDLING_HEIGHT *and*
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# while moving forward *and* facing the ball, i.e. a far narrower slice of
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# ticks than the penalty it replaced. Net pressure fell and so did
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# upright_fraction (0.268 -> 0.239 -> 0.238, the lowest of any round). The
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# conjunctive part worked though: forward_motion_fraction reached its best
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# sustained value (0.242) *without* goal_rate sagging, ep_rew_mean turned
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# positive for the first time (+0.28), and the eval win rate hit 49% with
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# no reward hacking. So round 5 restores ground-tilt-penalty to 0.05 and
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# changes nothing else — a genuine single-variable test of multiplier plus
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# full tilt pressure.
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#
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# Also added this round: grounded_upright_fraction, a *diagnostic, ungated*
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# telemetry signal measuring uprightness over real floor-contact ticks
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# rather than sub-3m ticks. upright_fraction has never exceeded 0.331
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# across four rounds and ~560M steps without the policy cheating, and its
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# denominator is dominated by ballistic transit (airborne_fraction ~0.45,
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# mean_altitude ~4.4m) where attitude is not meaningfully controllable —
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# so it likely cannot measure the behaviour the 0.45 floor was meant to
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# capture. Re-baseline that floor from what the new signal reports rather
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# than from another round of reshaping.
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#
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# Round 6 (2026-08-16): round 5 read grounded_upright_fraction 0.050 /
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# 0.069 / 0.052 — when the ship touches the floor it is upright about 1
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# time in 17 — and the user's own observation was "it spends the vast
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# majority of the time on its side, driving upwards towards the ball". A
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# critical review of the *simulation* rather than the reward found why six
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# rounds of shaping could never work:
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#
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# 1. The hull was a 1x1x4 box with inertia (1,1,1) and no restoring
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# torque anywhere, so belly-down and rolled-90 were geometrically
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# identical resting states. "Upright" was not a physically
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# distinguished state at all — the reward was paying for a property
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# the simulation did not have.
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# 2. ~65% of episodes spawned ships from _random_position, which samples
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# Y uniformly over the full 18m volume (mean ~8.7m). The measured
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# airborne_fraction ~0.44 was largely that spawn distribution, and
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# every ground-handling term fades out above 3m, so the shaping
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# being tuned barely ever applied.
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# 3. air_drill_chance 0.20 spawned deliberately unreachable-without-
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# climbing states in the stage meant to teach ground driving, and its
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# own air_touch_fraction (0.0002) shows the drills were never solved.
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#
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# Fixes land in the physics and the task distribution instead of the
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# reward: an altitude-faded righting torque plus a flat-bottomed hull and
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# realistic inertia (ship.gd / ship.tscn) make belly-down a genuine
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# attractor, ground_start_chance 0.50 actually starts the ship on the
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# floor, and air-drill-chance goes to 0. The reward terms already built
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# are left exactly as they were — they should finally pull in a direction
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# the ship can go.
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#
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# Round 7 (2026-08-18): Stage 4 closed by human override (see TRAINING.md).
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# Stage 5 (intercepts) then blocked all three attempts on the same single
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# floor every time — rollout/productive_air_touch_fraction stayed exactly
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# 0.0 across a continuous 180M-step lineage (each retry resumes the
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# previous attempt's checkpoint, not a fresh run), while air_touch_fraction
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# sat at noise level (0.00008 -> 0.00006 -> 0.00006) and goal_rate/
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# upright_fraction/forward_motion_fraction all kept improving on the same
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# budget. A dead-flat metric across that much continued training, next to
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# metrics that keep moving, is the missing-mechanism signature from Round 6
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# again, not a slow-learning one: forward_velocity_to_ball_weight -- the
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# term that actually solved ground handling -- is hard-gated to
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# ship.global_position.y < GROUND_HANDLING_HEIGHT and does nothing in the
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# air, so air_intercept_chance (added for Stage 5) was asking for aerial
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# pursuit with only the generic, orientation-agnostic velocity_to_ball_
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# weight (0.04) to learn it from -- the same class of gap as Stage 4's
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# missing ground-tilt/non-forward pressure before those were added.
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#
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# air_approach_weight (ship_ai_controller.gd) is the airborne mirror:
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# nose-first 3D closing speed on the ball, active above
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# GROUND_HANDLING_HEIGHT instead of below it (mutually exclusive with
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# forward_velocity_to_ball_weight by altitude), with no uprightness
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# multiplier since a real aerial requires pitching away from level. Set to
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# 0.15 to match forward_velocity_to_ball_weight's proven-effective
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# magnitude; added to HANDLING_REWARD_FLAGS (not just Stage 5's flags) so
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# it also carries into Stage 6, which reuses these flags and its own
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# air_intercept_chance. Stage 5 restarts from Stage 4's checkpoint rather
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# than continuing retry2's, same reasoning as every previous mechanism
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# change in this file: don't resume a policy shaped by an absent term into
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# one where it now exists.
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HANDLING_REWARD_FLAGS = [
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"--velocity-to-ball-weight", "0.04",
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"--forward-velocity-to-ball-weight", "0.15",
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"--air-approach-weight", "0.15",
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"--ball-distance-penalty", "0.01",
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"--ball-touch-reward", "0.7",
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"--ball-velocity-to-goal-weight", "0.06",
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"--goal-reward", "80",
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"--speed-reward-weight", "0.0",
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"--tilt-penalty", "0.0002",
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"--ground-tilt-penalty", "0.05",
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"--non-forward-penalty", "0.04",
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"--grounded-upright-reward", "0.0",
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]
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STAGES = [
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{
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"number": 4,
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"name": "handling",
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"timesteps": 40_000_000,
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"flags": [
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"--opponent-mode", "self_play",
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"--kickoff-chance", "0.15",
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"--near-goal-chance", "0.25",
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"--air-drill-chance", "0.0",
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"--air-intercept-chance", "0.0",
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"--ground-start-chance", "0.50",
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*HANDLING_REWARD_FLAGS,
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],
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# Conservative catastrophe floors, not claims of mastery. Tail values
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# are recorded in state so later thresholds can be based on evidence.
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"telemetry_floors": {
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"rollout/goal_rate": 0.80,
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"rollout/upright_fraction": 0.45,
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"rollout/forward_motion_fraction": 0.25,
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},
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# At least 80% of the paired candidate-vs-Stage-3 episodes must end
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# in a goal. This is separate from win-rate regression: a draw-heavy
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# handling policy must not advance merely because neither bot won.
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"evaluation_goal_rate_floor": 0.80,
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# The paired side swap also measures physical spawn/team bias. This
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# catches a broken team-frame action mapping even when model A's
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# aggregate result looks balanced because it plays both sides.
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"physical_side_imbalance_ceiling": 0.20,
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},
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{
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"number": 5,
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"name": "intercepts",
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"timesteps": 60_000_000,
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"flags": [
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"--opponent-mode", "self_play",
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"--kickoff-chance", "0.10",
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"--near-goal-chance", "0.20",
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"--air-drill-chance", "0.10",
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"--air-intercept-chance", "0.45",
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*HANDLING_REWARD_FLAGS,
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],
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"telemetry_floors": {
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"rollout/goal_rate": 0.75,
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"rollout/upright_fraction": 0.40,
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"rollout/forward_motion_fraction": 0.20,
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"rollout/productive_air_touch_fraction": 0.005,
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},
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"evaluation_goal_rate_floor": 0.75,
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"physical_side_imbalance_ceiling": 0.20,
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},
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{
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"number": 6,
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"name": "league",
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"timesteps": 100_000_000,
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"flags": [
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"--opponent-mode", "league",
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"--kickoff-chance", "0.15",
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"--near-goal-chance", "0.25",
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"--air-drill-chance", "0.15",
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"--air-intercept-chance", "0.25",
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*HANDLING_REWARD_FLAGS,
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],
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"telemetry_floors": {
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"rollout/goal_rate": 0.70,
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"rollout/upright_fraction": 0.35,
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"rollout/forward_motion_fraction": 0.18,
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"rollout/productive_air_touch_fraction": 0.003,
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},
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"evaluation_goal_rate_floor": 0.70,
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"physical_side_imbalance_ceiling": 0.20,
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"league_pool": True,
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},
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]
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def fresh_state() -> dict:
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return {"stage_index": 0, "attempt": 0, "status": "in_progress", "log": []}
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def load_state() -> dict:
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return json.loads(STATE_PATH.read_text()) if STATE_PATH.exists() else fresh_state()
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def save_state(state: dict) -> None:
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STATE_PATH.write_text(json.dumps(state, indent=2) + "\n")
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def passing_entry(state: dict, stage_index: int) -> dict:
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for entry in state["log"]:
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if entry["stage_index"] == stage_index and entry["decision"] == "pass":
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return entry
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raise RuntimeError(f"No passing generation-5 stage index {stage_index}")
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def previous_attempt_entry(state: dict, stage_index: int, attempt: int) -> dict:
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for entry in reversed(state["log"]):
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if entry["stage_index"] == stage_index and entry["attempt"] == attempt - 1:
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return entry
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raise RuntimeError(f"No previous attempt for stage index {stage_index}, attempt {attempt}")
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def resume_checkpoint(state: dict, stage_index: int, attempt: int, foundation: pathlib.Path) -> pathlib.Path:
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if attempt > 0:
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exp = previous_attempt_entry(state, stage_index, attempt)["experiment"]
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return TRAINING_DIR / "checkpoints" / exp / "final.zip"
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if stage_index == 0:
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return foundation
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exp = passing_entry(state, stage_index - 1)["experiment"]
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return TRAINING_DIR / "checkpoints" / exp / "final.zip"
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def reference_export(state: dict, stage_index: int) -> pathlib.Path:
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if stage_index == 0:
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return PROMOTED_EASY
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exp = passing_entry(state, stage_index - 1)["experiment"]
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return REPO_ROOT / "Game" / "bots" / f"{exp}.json"
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def league_pool(state: dict) -> list[pathlib.Path]:
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stage4 = passing_entry(state, 0)["experiment"]
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stage5 = passing_entry(state, 1)["experiment"]
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return [
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FOUNDATION_EXPORT,
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REPO_ROOT / "Game" / "bots" / f"{stage4}.json",
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REPO_ROOT / "Game" / "bots" / f"{stage5}.json",
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]
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def telemetry_tail(experiment: str, count: int = 500) -> dict[str, float]:
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log_dirs = sorted((TRAINING_DIR / "logs").glob(f"{experiment}_*"))
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if not log_dirs:
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return {}
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event_files = sorted(log_dirs[-1].glob("events.out.tfevents.*"))
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if not event_files:
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return {}
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accumulator = EventAccumulator(str(event_files[-1]), size_guidance={"scalars": 0})
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accumulator.Reload()
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result = {}
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for tag in accumulator.Tags().get("scalars", []):
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if not tag.startswith("rollout/"):
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continue
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values = [point.value for point in accumulator.Scalars(tag)[-count:]]
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if values:
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result[tag] = sum(values) / len(values)
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return result
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def telemetry_passes(stage: dict, telemetry: dict[str, float]) -> tuple[bool, list[str]]:
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failures = []
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for metric, floor in stage.get("telemetry_floors", {}).items():
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value = telemetry.get(metric)
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if value is None:
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failures.append(f"{metric} missing")
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elif value < floor:
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failures.append(f"{metric}={value:.4f} < {floor:.4f}")
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return not failures, failures
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def run_training(state: dict, stage_index: int, attempt: int, args) -> str:
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stage = STAGES[stage_index]
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suffix = "" if attempt == 0 else f"-retry{attempt}"
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experiment = f"{datetime.now().strftime('%Y%m%d-%H%M')}-gen5-s{stage['number']}-{stage['name']}{suffix}"
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resume = resume_checkpoint(state, stage_index, attempt, pathlib.Path(args.foundation_checkpoint))
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if not resume.exists():
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raise FileNotFoundError(f"Resume checkpoint not found: {resume}")
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cmd = [
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"./run_training.sh", experiment,
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"--timesteps", str(stage["timesteps"]),
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"--n-parallel", str(args.n_parallel),
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"--speedup", str(args.speedup),
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"--resume", str(resume),
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*STANDING_ARGS,
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*stage["flags"],
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]
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if stage.get("league_pool"):
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pool = league_pool(state)
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missing = [str(path) for path in pool if not path.exists()]
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if missing:
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raise FileNotFoundError(f"League pool models missing: {missing}")
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cmd += ["--opponent-pool", ",".join(str(path) for path in pool)]
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print(f"\n=== Generation 5 Stage {stage['number']} {stage['name']} attempt {attempt + 1} ===")
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print(" ".join(cmd))
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if args.dry_run:
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return experiment
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subprocess.run(cmd, cwd=TRAINING_DIR, check=True)
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return experiment
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def evaluate(experiment: str, reference: pathlib.Path, args) -> dict:
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candidate = REPO_ROOT / "Game" / "bots" / f"{experiment}.json"
|
|
cmd = [
|
|
".venv/bin/python", "evaluate.py", str(candidate), str(reference),
|
|
"--episodes", str(EVAL_EPISODES), "--speedup", str(args.speedup),
|
|
]
|
|
if args.godot_bin:
|
|
cmd += ["--godot_bin", args.godot_bin]
|
|
subprocess.run(cmd, cwd=TRAINING_DIR, check=True)
|
|
return json.loads(EVAL_HISTORY_PATH.read_text())[-1]
|
|
|
|
|
|
def match_passes(record: dict) -> bool:
|
|
candidate = record["wins_a"] / record["episodes"]
|
|
reference = record["wins_b"] / record["episodes"]
|
|
return reference - candidate < REGRESSION_MARGIN
|
|
|
|
|
|
def evaluation_goal_rate(record: dict) -> float:
|
|
"""Fraction of paired evaluation episodes that ended in either bot scoring."""
|
|
return (record["wins_a"] + record["wins_b"]) / record["episodes"]
|
|
|
|
|
|
def physical_side_imbalance(record: dict) -> float:
|
|
"""Absolute physical-team win margin as a fraction of all episodes."""
|
|
physical = record["physical_team_wins"]
|
|
return abs(physical["team_0"] - physical["team_1"]) / record["episodes"]
|
|
|
|
|
|
def commit_progress(experiment: str) -> None:
|
|
subprocess.run(["git", "add", STATE_PATH.name, EVAL_HISTORY_PATH.name], cwd=TRAINING_DIR, check=True)
|
|
if subprocess.run(["git", "diff", "--cached", "--quiet"], cwd=TRAINING_DIR).returncode == 0:
|
|
return
|
|
subprocess.run(
|
|
["git", "commit", "-m", f"chore(training): generation 5 progress after {experiment}"],
|
|
cwd=TRAINING_DIR,
|
|
check=True,
|
|
)
|
|
subprocess.run(["git", "push"], cwd=TRAINING_DIR, check=True)
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument("--n-parallel", type=int, default=14)
|
|
parser.add_argument("--speedup", type=int, default=16)
|
|
parser.add_argument("--godot-bin", default=None, help="Godot binary for post-stage evaluation")
|
|
parser.add_argument("--foundation-checkpoint", default=str(FOUNDATION_CHECKPOINT))
|
|
parser.add_argument("--force-retry", action="store_true")
|
|
parser.add_argument("--skip-to-next-stage", action="store_true")
|
|
parser.add_argument("--dry-run", action="store_true", help="Print the next run command without executing it")
|
|
args = parser.parse_args()
|
|
|
|
state = load_state()
|
|
if state["status"] == "done":
|
|
print("Generation 5 is already complete.")
|
|
return
|
|
if state["status"] == "blocked":
|
|
if args.force_retry:
|
|
state["attempt"] += 1
|
|
state["status"] = "in_progress"
|
|
save_state(state)
|
|
elif args.skip_to_next_stage:
|
|
state["stage_index"] += 1
|
|
state["attempt"] = 0
|
|
state["status"] = "in_progress"
|
|
save_state(state)
|
|
else:
|
|
stage = STAGES[state["stage_index"]]
|
|
print(f"BLOCKED at Stage {stage['number']} {stage['name']}; inspect {STATE_PATH.name}.")
|
|
print("Use --force-retry after adjustment or --skip-to-next-stage after human review.")
|
|
sys.exit(1)
|
|
|
|
while state["stage_index"] < len(STAGES):
|
|
stage_index = state["stage_index"]
|
|
attempt = state["attempt"]
|
|
stage = STAGES[stage_index]
|
|
experiment = run_training(state, stage_index, attempt, args)
|
|
if args.dry_run:
|
|
return
|
|
|
|
telemetry = telemetry_tail(experiment)
|
|
telemetry_ok, telemetry_failures = telemetry_passes(stage, telemetry)
|
|
references = [reference_export(state, stage_index)]
|
|
if stage.get("league_pool"):
|
|
references.extend(league_pool(state))
|
|
# Preserve order while avoiding a duplicate Stage-5 evaluation in
|
|
# the league stage (its predecessor is also in the pool).
|
|
references = list(dict.fromkeys(references))
|
|
records = [evaluate(experiment, reference, args) for reference in references]
|
|
match_ok = all(match_passes(record) for record in records)
|
|
evaluation_goal_floor = stage.get("evaluation_goal_rate_floor", 0.0)
|
|
evaluation_goal_failures = [
|
|
f"{pathlib.Path(record['model_b']).name}: goal_rate={evaluation_goal_rate(record):.3f} "
|
|
f"< {evaluation_goal_floor:.3f}"
|
|
for record in records
|
|
if evaluation_goal_rate(record) < evaluation_goal_floor
|
|
]
|
|
scoring_ok = not evaluation_goal_failures
|
|
side_imbalance_ceiling = stage.get("physical_side_imbalance_ceiling", 1.0)
|
|
side_balance_failures = [
|
|
f"{pathlib.Path(record['model_b']).name}: physical_side_imbalance="
|
|
f"{physical_side_imbalance(record):.3f} > {side_imbalance_ceiling:.3f}"
|
|
for record in records
|
|
if physical_side_imbalance(record) > side_imbalance_ceiling
|
|
]
|
|
side_balance_ok = not side_balance_failures
|
|
decision = "pass" if match_ok and telemetry_ok else "fail"
|
|
if not scoring_ok or not side_balance_ok:
|
|
decision = "fail"
|
|
entry = {
|
|
"stage_index": stage_index,
|
|
"stage_number": stage["number"],
|
|
"stage_name": stage["name"],
|
|
"experiment": experiment,
|
|
"attempt": attempt,
|
|
"telemetry_tail": telemetry,
|
|
"telemetry_failures": telemetry_failures,
|
|
"evaluation_goal_failures": evaluation_goal_failures,
|
|
"side_balance_failures": side_balance_failures,
|
|
"eval": records[0],
|
|
"evals": records,
|
|
"decision": decision,
|
|
}
|
|
state["log"].append(entry)
|
|
print(
|
|
f"{experiment}: match={'pass' if match_ok else 'fail'}, "
|
|
f"scoring={'pass' if scoring_ok else 'fail'}, "
|
|
f"side_balance={'pass' if side_balance_ok else 'fail'}, "
|
|
f"telemetry={'pass' if telemetry_ok else 'fail'} -> {decision}"
|
|
)
|
|
for failure in telemetry_failures:
|
|
print(f" {failure}")
|
|
for failure in evaluation_goal_failures:
|
|
print(f" {failure}")
|
|
for failure in side_balance_failures:
|
|
print(f" {failure}")
|
|
|
|
if decision == "pass":
|
|
state["stage_index"] += 1
|
|
state["attempt"] = 0
|
|
save_state(state)
|
|
commit_progress(experiment)
|
|
continue
|
|
if attempt >= MAX_RETRIES:
|
|
state["status"] = "blocked"
|
|
save_state(state)
|
|
commit_progress(experiment)
|
|
print(f"BLOCKED after {MAX_RETRIES + 1} attempts at Stage {stage['number']}.")
|
|
sys.exit(1)
|
|
state["attempt"] += 1
|
|
save_state(state)
|
|
commit_progress(experiment)
|
|
|
|
state["status"] = "done"
|
|
save_state(state)
|
|
commit_progress(state["log"][-1]["experiment"])
|
|
print("Generation 5 complete: handling, intercepts, and league stages passed.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|