mirror of
https://github.com/jcreek/CosmicClash.git
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feat(training): add generation 5 curriculum
This commit is contained in:
Josh Creek
@@ -0,0 +1,407 @@
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"""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 and adding a stronger floor-local term.
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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", "0.06",
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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", "0.003",
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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", "0.20",
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"--air-intercept-chance", "0.0",
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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"
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cmd = [
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".venv/bin/python", "evaluate.py", str(candidate), str(reference),
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"--episodes", str(EVAL_EPISODES), "--speedup", str(args.speedup),
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]
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if args.godot_bin:
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cmd += ["--godot_bin", args.godot_bin]
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subprocess.run(cmd, cwd=TRAINING_DIR, check=True)
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return json.loads(EVAL_HISTORY_PATH.read_text())[-1]
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def match_passes(record: dict) -> bool:
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candidate = record["wins_a"] / record["episodes"]
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reference = record["wins_b"] / record["episodes"]
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return reference - candidate < REGRESSION_MARGIN
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def evaluation_goal_rate(record: dict) -> float:
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"""Fraction of paired evaluation episodes that ended in either bot scoring."""
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return (record["wins_a"] + record["wins_b"]) / record["episodes"]
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def physical_side_imbalance(record: dict) -> float:
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"""Absolute physical-team win margin as a fraction of all episodes."""
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physical = record["physical_team_wins"]
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return abs(physical["team_0"] - physical["team_1"]) / record["episodes"]
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def commit_progress(experiment: str) -> None:
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subprocess.run(["git", "add", STATE_PATH.name, EVAL_HISTORY_PATH.name], cwd=TRAINING_DIR, check=True)
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if subprocess.run(["git", "diff", "--cached", "--quiet"], cwd=TRAINING_DIR).returncode == 0:
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return
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subprocess.run(
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["git", "commit", "-m", f"chore(training): generation 5 progress after {experiment}"],
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cwd=TRAINING_DIR,
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check=True,
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)
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subprocess.run(["git", "push"], cwd=TRAINING_DIR, check=True)
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--n-parallel", type=int, default=14)
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parser.add_argument("--speedup", type=int, default=16)
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parser.add_argument("--godot-bin", default=None, help="Godot binary for post-stage evaluation")
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parser.add_argument("--foundation-checkpoint", default=str(FOUNDATION_CHECKPOINT))
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parser.add_argument("--force-retry", action="store_true")
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parser.add_argument("--skip-to-next-stage", action="store_true")
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parser.add_argument("--dry-run", action="store_true", help="Print the next run command without executing it")
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args = parser.parse_args()
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state = load_state()
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if state["status"] == "done":
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print("Generation 5 is already complete.")
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return
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if state["status"] == "blocked":
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if args.force_retry:
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state["attempt"] += 1
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state["status"] = "in_progress"
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save_state(state)
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elif args.skip_to_next_stage:
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state["stage_index"] += 1
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state["attempt"] = 0
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state["status"] = "in_progress"
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save_state(state)
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else:
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stage = STAGES[state["stage_index"]]
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print(f"BLOCKED at Stage {stage['number']} {stage['name']}; inspect {STATE_PATH.name}.")
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print("Use --force-retry after adjustment or --skip-to-next-stage after human review.")
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sys.exit(1)
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while state["stage_index"] < len(STAGES):
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stage_index = state["stage_index"]
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attempt = state["attempt"]
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stage = STAGES[stage_index]
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experiment = run_training(state, stage_index, attempt, args)
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if args.dry_run:
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return
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telemetry = telemetry_tail(experiment)
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telemetry_ok, telemetry_failures = telemetry_passes(stage, telemetry)
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references = [reference_export(state, stage_index)]
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if stage.get("league_pool"):
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references.extend(league_pool(state))
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# Preserve order while avoiding a duplicate Stage-5 evaluation in
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# the league stage (its predecessor is also in the pool).
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references = list(dict.fromkeys(references))
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records = [evaluate(experiment, reference, args) for reference in references]
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match_ok = all(match_passes(record) for record in records)
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evaluation_goal_floor = stage.get("evaluation_goal_rate_floor", 0.0)
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evaluation_goal_failures = [
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f"{pathlib.Path(record['model_b']).name}: goal_rate={evaluation_goal_rate(record):.3f} "
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f"< {evaluation_goal_floor:.3f}"
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for record in records
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if evaluation_goal_rate(record) < evaluation_goal_floor
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]
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scoring_ok = not evaluation_goal_failures
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side_imbalance_ceiling = stage.get("physical_side_imbalance_ceiling", 1.0)
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side_balance_failures = [
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f"{pathlib.Path(record['model_b']).name}: physical_side_imbalance="
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f"{physical_side_imbalance(record):.3f} > {side_imbalance_ceiling:.3f}"
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for record in records
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if physical_side_imbalance(record) > side_imbalance_ceiling
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]
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side_balance_ok = not side_balance_failures
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decision = "pass" if match_ok and telemetry_ok else "fail"
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if not scoring_ok or not side_balance_ok:
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decision = "fail"
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entry = {
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"stage_index": stage_index,
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"stage_number": stage["number"],
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"stage_name": stage["name"],
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"experiment": experiment,
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"attempt": attempt,
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"telemetry_tail": telemetry,
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"telemetry_failures": telemetry_failures,
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"evaluation_goal_failures": evaluation_goal_failures,
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"side_balance_failures": side_balance_failures,
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"eval": records[0],
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"evals": records,
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"decision": decision,
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}
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state["log"].append(entry)
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print(
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f"{experiment}: match={'pass' if match_ok else 'fail'}, "
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f"scoring={'pass' if scoring_ok else 'fail'}, "
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f"side_balance={'pass' if side_balance_ok else 'fail'}, "
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f"telemetry={'pass' if telemetry_ok else 'fail'} -> {decision}"
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)
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for failure in telemetry_failures:
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print(f" {failure}")
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for failure in evaluation_goal_failures:
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print(f" {failure}")
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for failure in side_balance_failures:
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print(f" {failure}")
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if decision == "pass":
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state["stage_index"] += 1
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state["attempt"] = 0
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save_state(state)
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commit_progress(experiment)
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continue
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if attempt >= MAX_RETRIES:
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state["status"] = "blocked"
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save_state(state)
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commit_progress(experiment)
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print(f"BLOCKED after {MAX_RETRIES + 1} attempts at Stage {stage['number']}.")
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sys.exit(1)
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state["attempt"] += 1
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save_state(state)
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commit_progress(experiment)
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state["status"] = "done"
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save_state(state)
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commit_progress(state["log"][-1]["experiment"])
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print("Generation 5 complete: handling, intercepts, and league stages passed.")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user