File size: 3,220 Bytes
02f4a63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | """Build bench export JSON from a final game observation.
Custom agents that use OpenRAEnv directly (CNN, RL, multi-agent, etc.)
can call build_bench_export() after their game loop to produce a bench
submission JSON — the same format the built-in LLM agent auto-exports.
Usage:
from openra_env.bench_export import build_bench_export
obs = await env.step(action) # final observation (obs.done == True)
export = build_bench_export(
obs,
agent_name="DeathBot-9000",
agent_type="RL",
opponent="Normal",
)
print(f"Saved to {export['path']}")
# Then submit:
# openra-rl bench submit <path>
"""
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Optional
def build_bench_export(
obs: Any,
agent_name: str,
agent_type: str = "RL",
opponent: str = "Normal",
agent_url: str = "",
replay_path: str = "",
export_dir: Optional[Path] = None,
) -> Dict[str, Any]:
"""Build and save a bench export JSON from a final observation.
Args:
obs: Final observation — either a dict or a Pydantic model with
.military, .economy, .tick, .result, .explored_percent attributes.
agent_name: Display name for the leaderboard.
agent_type: One of "Scripted", "LLM", "RL".
opponent: Difficulty tier (Beginner/Easy/Medium/Normal/Hard).
agent_url: Optional GitHub/project URL.
replay_path: Optional path to .orarep replay file.
export_dir: Where to save the JSON (default: ~/.openra-rl/bench-exports/).
Returns:
Dict with all submission fields plus "path" pointing to the saved file.
"""
# Normalize obs to dict
if hasattr(obs, "model_dump"):
obs_dict = obs.model_dump()
elif hasattr(obs, "__dict__") and not isinstance(obs, dict):
obs_dict = vars(obs)
else:
obs_dict = dict(obs)
mil = obs_dict.get("military") or {}
kills = mil.get("kills_cost", 0)
deaths = mil.get("deaths_cost", 0)
sub = {
"agent_name": agent_name,
"agent_type": agent_type,
"agent_url": agent_url,
"opponent": opponent,
"games": 1,
"result": obs_dict.get("result", ""),
"win": obs_dict.get("result") == "win",
"ticks": obs_dict.get("tick", 0),
"kills_cost": kills,
"deaths_cost": deaths,
"kd_ratio": round(kills / max(deaths, 1), 2),
"assets_value": mil.get("assets_value", 0),
"explored_percent": obs_dict.get("explored_percent", 0),
"reward_vector": obs_dict.get("reward_vector", {}),
"replay_path": replay_path,
"timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
}
# Save to disk
if export_dir is None:
export_dir = Path.home() / ".openra-rl" / "bench-exports"
export_dir.mkdir(parents=True, exist_ok=True)
ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
slug = agent_name.replace("/", "_").replace(" ", "_")[:40]
export_path = export_dir / f"bench-{slug}-{ts}.json"
export_path.write_text(json.dumps(sub, indent=2))
sub["path"] = str(export_path)
return sub
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