mirror of
https://github.com/jcreek/CosmicClash.git
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180 lines
6.6 KiB
Python
180 lines
6.6 KiB
Python
"""Pit two exported policies against each other and record the result.
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Uses the same in-Godot inference path that ships in the game
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(AIShipController + PolicyNetwork), so eval strength = in-game strength.
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Episodes are golden-goal: first goal wins, timeout is a draw. Every randomized
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starting state is played twice with sides swapped, so physical-team or arena
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asymmetry cannot be mistaken for model strength. Results are appended to
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eval_history.json — the bot-progress-over-time record.
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Example:
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.venv/bin/python evaluate.py ../Game/bots/rookie.json checkpoints/run01/candidate.json --episodes 40
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"""
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import argparse
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import datetime
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import json
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import os
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import pathlib
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import subprocess
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TRAINING_DIR = pathlib.Path(__file__).resolve().parent
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GAME_DIR = TRAINING_DIR.parent / "Game"
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TRAINING_SCENE = "res://scenes/training.tscn"
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DEFAULT_GODOT_MACOS = "/Applications/Godot.app/Contents/MacOS/Godot"
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def run_half(
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godot_bin: str,
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model_a: str,
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model_b: str,
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episodes: int,
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speedup: int,
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seed: int,
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grounded_a: bool = False,
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grounded_b: bool = False,
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) -> dict:
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cmd = [
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godot_bin,
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"--path",
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str(GAME_DIR),
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TRAINING_SCENE,
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"--headless",
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"--disable-render-loop",
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f"--eval_model_a={model_a}",
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f"--eval_model_b={model_b}",
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f"--eval_episodes={episodes}",
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f"--speedup={speedup}",
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f"--env_seed={seed}",
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]
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# Must match how each model was actually trained (see AIShipController's
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# allow_vertical/allow_pitch_roll) — a grounded pre-generation-4 model
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# never got a reward gradient on these axes, so leaving them unmasked here
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# adds untrained aerial noise its training never had to contend with.
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if grounded_a:
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cmd += ["--eval_allow_vertical_a=false", "--eval_allow_pitch_roll_a=false"]
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if grounded_b:
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cmd += ["--eval_allow_vertical_b=false", "--eval_allow_pitch_roll_b=false"]
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timeout = episodes * 30 / speedup * 3 + 120 # worst case: all draws, plus margin
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
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for line in result.stdout.splitlines():
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if line.startswith("EVAL_RESULT "):
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return json.loads(line[len("EVAL_RESULT "):])
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raise RuntimeError(f"No EVAL_RESULT in godot output:\n{result.stdout[-2000:]}\n{result.stderr[-2000:]}")
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def evaluate_pair(
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godot_bin: str,
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model_a: str,
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model_b: str,
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episodes: int,
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speedup: int,
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seed: int,
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grounded_a: bool = False,
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grounded_b: bool = False,
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) -> dict:
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"""Replay one seeded state sequence with the models on opposite sides."""
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if episodes < 2 or episodes % 2 != 0:
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raise ValueError("--episodes must be an even number of at least 2 for paired side swaps")
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episodes_per_side = episodes // 2
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first = run_half(
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godot_bin, model_a, model_b, episodes_per_side, speedup, seed,
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grounded_a=grounded_a, grounded_b=grounded_b,
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)
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second = run_half(
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godot_bin, model_b, model_a, episodes_per_side, speedup, seed,
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grounded_a=grounded_b, grounded_b=grounded_a,
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)
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a_team_0 = {
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"wins_a": first["goals_a"],
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"wins_b": first["goals_b"],
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"draws": first["draws"],
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}
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a_team_1 = {
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"wins_a": second["goals_b"],
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"wins_b": second["goals_a"],
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"draws": second["draws"],
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}
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record = {
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"timestamp": datetime.datetime.now(datetime.timezone.utc).isoformat(timespec="seconds"),
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"model_a": model_a,
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"model_b": model_b,
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"seed": seed,
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"episodes": first["episodes"] + second["episodes"],
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"wins_a": a_team_0["wins_a"] + a_team_1["wins_a"],
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"wins_b": a_team_0["wins_b"] + a_team_1["wins_b"],
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"draws": a_team_0["draws"] + a_team_1["draws"],
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"side_results": {
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"a_team_0": a_team_0,
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"a_team_1": a_team_1,
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},
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"physical_team_wins": {
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"team_0": first["goals_a"] + second["goals_a"],
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"team_1": first["goals_b"] + second["goals_b"],
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},
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}
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record["win_rate_a"] = round(record["wins_a"] / record["episodes"], 3)
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return record
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def main():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("model_a", help="Path to first exported policy .json")
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parser.add_argument("model_b", help="Path to second exported policy .json")
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parser.add_argument(
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"--episodes", type=int, default=20,
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help="Total episodes; must be even so every seeded state is replayed with sides swapped",
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)
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parser.add_argument(
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"--godot_bin",
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default=os.environ.get("GODOT_BIN", DEFAULT_GODOT_MACOS),
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help="Path to the Godot binary (or set GODOT_BIN)",
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)
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parser.add_argument("--speedup", type=int, default=16)
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parser.add_argument("--seed", type=int, default=1, help="Seed for the paired starting-state sequence")
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parser.add_argument("--history", default=str(TRAINING_DIR / "eval_history.json"))
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parser.add_argument(
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"--grounded-a", action="store_true", help="model_a was trained with locomotion masked (pre-generation-4 models)"
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)
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parser.add_argument(
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"--grounded-b", action="store_true", help="model_b was trained with locomotion masked (pre-generation-4 models)"
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)
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args = parser.parse_args()
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model_a = str(pathlib.Path(args.model_a).resolve())
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model_b = str(pathlib.Path(args.model_b).resolve())
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try:
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record = evaluate_pair(
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args.godot_bin, model_a, model_b, args.episodes, args.speedup, args.seed,
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grounded_a=args.grounded_a, grounded_b=args.grounded_b,
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)
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except ValueError as error:
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parser.error(str(error))
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history_path = pathlib.Path(args.history)
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history = json.loads(history_path.read_text()) if history_path.exists() else []
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history.append(record)
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history_path.write_text(json.dumps(history, indent=2) + "\n")
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print(
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f"{pathlib.Path(model_a).name} vs {pathlib.Path(model_b).name} over {record['episodes']} episodes: "
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f"{record['wins_a']}-{record['wins_b']} ({record['draws']} draws), "
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f"win rate A = {record['win_rate_a']:.0%}"
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)
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a_team_0 = record["side_results"]["a_team_0"]
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a_team_1 = record["side_results"]["a_team_1"]
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physical = record["physical_team_wins"]
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print(
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f"Paired seed {record['seed']} side split: "
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f"A as team 0 {a_team_0['wins_a']}-{a_team_0['wins_b']} ({a_team_0['draws']} draws); "
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f"A as team 1 {a_team_1['wins_a']}-{a_team_1['wins_b']} ({a_team_1['draws']} draws); "
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f"physical teams 0-1 = {physical['team_0']}-{physical['team_1']}"
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)
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print(f"Appended to {history_path}")
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if __name__ == "__main__":
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main()
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