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#!/usr/bin/env python3
"""Generate deterministic BabyAI expert demonstrations for shared feedback."""
import argparse
import json
from pathlib import Path
import sys
import time
import gymnasium as gym
import minigrid
import numpy as np
from minigrid.core.constants import COLOR_TO_IDX, OBJECT_TO_IDX, STATE_TO_IDX
from minigrid.utils.baby_ai_bot import BabyAIBot
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from sdil.babyai_shared import build_vocabulary, missions_to_bow
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_OUT = ROOT / "data" / "babyai_shared" / "goto_obj_s6_b0.npz"
def generate_split(env_id, seeds):
images = []
directions = []
missions = []
actions = []
episode_offsets = [0]
episode_returns = []
env = gym.make(env_id)
try:
for seed in seeds:
observation, _ = env.reset(seed=int(seed))
bot = BabyAIBot(env)
previous_action = None
episode_return = 0.0
finished = False
for _ in range(env.unwrapped.max_steps):
action = bot.replan(previous_action)
images.append(observation["image"].copy())
directions.append(int(observation["direction"]))
missions.append(str(observation["mission"]))
actions.append(int(action))
observation, reward, terminated, truncated, _ = env.step(int(action))
episode_return += float(reward)
previous_action = action
if terminated or truncated:
finished = True
break
if not finished or episode_return <= 0:
raise RuntimeError(
f"BabyAIBot failed env={env_id} seed={seed} "
f"return={episode_return}")
episode_offsets.append(len(actions))
episode_returns.append(episode_return)
finally:
env.close()
return {
"image": np.asarray(images, dtype=np.uint8),
"direction": np.asarray(directions, dtype=np.uint8),
"mission_text": np.asarray(missions),
"action": np.asarray(actions, dtype=np.uint8),
"episode_offset": np.asarray(episode_offsets, dtype=np.int64),
"episode_return": np.asarray(episode_returns, dtype=np.float32),
"episode_seed": np.asarray(list(seeds), dtype=np.int64),
}
def prefix(values, name):
return {f"{name}_{key}": value for key, value in values.items()}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--env-id", default="BabyAI-GoToObjS6-v1")
parser.add_argument("--train-episodes", type=int, default=20_000)
parser.add_argument("--validation-episodes", type=int, default=2_000)
parser.add_argument("--train-seed-start", type=int, default=0)
parser.add_argument("--validation-seed-start", type=int, default=100_000)
parser.add_argument("--rollout-seed-start", type=int, default=200_000)
parser.add_argument("--rollout-episodes", type=int, default=500)
parser.add_argument("--out", type=Path, default=DEFAULT_OUT)
args = parser.parse_args()
if args.train_seed_start + args.train_episodes > args.validation_seed_start:
raise ValueError("training and validation episode seeds overlap")
if (args.validation_seed_start + args.validation_episodes
> args.rollout_seed_start):
raise ValueError("validation demonstrations and rollouts overlap")
started = time.time()
train_seeds = range(
args.train_seed_start, args.train_seed_start + args.train_episodes)
validation_seeds = range(
args.validation_seed_start,
args.validation_seed_start + args.validation_episodes)
train = generate_split(args.env_id, train_seeds)
validation = generate_split(args.env_id, validation_seeds)
vocabulary = build_vocabulary(train["mission_text"])
train["mission_bow"] = missions_to_bow(
train.pop("mission_text"), vocabulary)
validation["mission_bow"] = missions_to_bow(
validation.pop("mission_text"), vocabulary)
metadata = {
"protocol": "babyai_shared_feedback_b0",
"env_id": args.env_id,
"minigrid_version": minigrid.__version__,
"train_episodes": args.train_episodes,
"validation_episodes": args.validation_episodes,
"train_steps": int(len(train["action"])),
"validation_steps": int(len(validation["action"])),
"rollout_seed_start": args.rollout_seed_start,
"rollout_episodes": args.rollout_episodes,
"object_cardinality": max(OBJECT_TO_IDX.values()) + 1,
"color_cardinality": max(COLOR_TO_IDX.values()) + 1,
"state_cardinality": max(STATE_TO_IDX.values()) + 1,
"vocabulary": list(vocabulary),
"elapsed_seconds": time.time() - started,
}
payload = {
**prefix(train, "train"),
**prefix(validation, "validation"),
"rollout_seed": np.arange(
args.rollout_seed_start,
args.rollout_seed_start + args.rollout_episodes,
dtype=np.int64),
"metadata_json": np.asarray(json.dumps(metadata, sort_keys=True)),
}
args.out.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(args.out, **payload)
print(json.dumps({"out": str(args.out), **metadata}, indent=2))
if __name__ == "__main__":
main()
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