File size: 34,020 Bytes
8efdae2 | 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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 | {
"cells": [
{
"cell_type": "markdown",
"id": "3905a08b",
"metadata": {},
"source": [
"# Train a Flatmate RL Action Policy with TRL\n",
"\n",
"This notebook connects to the Hugging Face Space endpoint, collects rollout examples over OpenEnv websocket sessions, and fine-tunes a small causal language model to emit Flatmate RL JSON actions. The training path uses TRL `SFTTrainer`, which is the most stable starting point for this mixed natural-language plus structured-tool action space.\n",
"\n",
"Endpoint used here: `https://huggingface.co/spaces/kushalExplores/flatmate_rl`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54f0ddc0",
"metadata": {},
"outputs": [],
"source": [
"# Install notebook dependencies. Restart the kernel after this cell if Colab/Jupyter asks you to.\n",
"%pip install -q \"trl>=0.23.0\" \"transformers>=4.46.0\" accelerate datasets peft websockets huggingface_hub matplotlib pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6a37c34",
"metadata": {},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"\n",
"import asyncio\n",
"import json\n",
"import random\n",
"from dataclasses import dataclass\n",
"from pathlib import Path\n",
"from typing import Any\n",
"from urllib.parse import urlparse\n",
"\n",
"import websockets\n",
"from datasets import Dataset\n",
"\n",
"SPACE_HTTP_URL = \"https://kushalexplores-flatmate-rl.hf.space\"\n",
"SCENARIOS = [\n",
" \"task_visit_single\",\n",
" \"task_visit_single_hidden_flex\",\n",
" \"task_visit_multi\",\n",
" \"task_visit_single_seller_followup\",\n",
"]\n",
"\n",
"def ws_url_from_http(base_url: str) -> str:\n",
" parsed = urlparse(base_url.rstrip(\"/\"))\n",
" scheme = \"wss\" if parsed.scheme == \"https\" else \"ws\"\n",
" return f\"{scheme}://{parsed.netloc}/ws\"\n",
"\n",
"SPACE_WS_URL = ws_url_from_http(SPACE_HTTP_URL)\n",
"SPACE_WS_URL"
]
},
{
"cell_type": "markdown",
"id": "3e10f23e",
"metadata": {},
"source": [
"## Endpoint Client\n",
"\n",
"OpenEnv's plain HTTP `/reset` and `/step` endpoints are stateless. Use `/ws` for multi-step episodes because the websocket session keeps one environment instance alive across reset and step calls."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f958cca7",
"metadata": {},
"outputs": [],
"source": [
"class FlatmateEndpoint:\n",
" def __init__(self, ws_url: str = SPACE_WS_URL, timeout_s: float = 120.0):\n",
" self.ws_url = ws_url\n",
" self.timeout_s = timeout_s\n",
"\n",
" async def __aenter__(self):\n",
" self.ws = await websockets.connect(self.ws_url, open_timeout=self.timeout_s, ping_timeout=self.timeout_s)\n",
" return self\n",
"\n",
" async def __aexit__(self, exc_type, exc, tb):\n",
" try:\n",
" await self.ws.send(json.dumps({\"type\": \"close\"}))\n",
" finally:\n",
" await self.ws.close()\n",
"\n",
" async def _send(self, payload: dict[str, Any]) -> dict[str, Any]:\n",
" await self.ws.send(json.dumps(payload))\n",
" raw = await asyncio.wait_for(self.ws.recv(), timeout=self.timeout_s)\n",
" message = json.loads(raw)\n",
" if message.get(\"type\") == \"error\":\n",
" raise RuntimeError(message.get(\"data\", message))\n",
" data = message[\"data\"]\n",
" obs = data.get(\"observation\", {})\n",
" obs[\"reward\"] = data.get(\"reward\")\n",
" obs[\"done\"] = data.get(\"done\", False)\n",
" return obs\n",
"\n",
" async def reset(self, scenario_id: str, seed: int | None = None) -> dict[str, Any]:\n",
" data: dict[str, Any] = {\"scenario_id\": scenario_id}\n",
" if seed is not None:\n",
" data[\"seed\"] = seed\n",
" return await self._send({\"type\": \"reset\", \"data\": data})\n",
"\n",
" async def step(self, action: dict[str, Any]) -> dict[str, Any]:\n",
" return await self._send({\"type\": \"step\", \"data\": action})\n",
"\n",
"async def smoke_test_endpoint():\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(\"task_visit_single\", seed=1)\n",
" print(obs[\"scenario_id\"], obs[\"status\"])\n",
" print(obs.get(\"last_user_message\") or obs.get(\"current_user_request\"))\n",
"\n",
"await smoke_test_endpoint()"
]
},
{
"cell_type": "markdown",
"id": "fe2ad079",
"metadata": {},
"source": [
"## Rollout Policy for Data Collection\n",
"\n",
"This heuristic is intentionally simple. It produces valid-looking action examples from endpoint observations; after SFT, replace it with model generation and keep the same evaluator."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "611b1ac4",
"metadata": {},
"outputs": [],
"source": [
"def tool_names(obs: dict[str, Any]) -> list[str]:\n",
" return [str(t.get(\"tool\", t.get(\"tool_name\", \"\"))) for t in obs.get(\"tool_trace\", [])]\n",
"\n",
"def action_policy(obs: dict[str, Any]) -> dict[str, Any] | None:\n",
" tools = tool_names(obs)\n",
" phase = obs.get(\"phase\", \"buyer\")\n",
" remaining = set(obs.get(\"remaining_required_fields\", []))\n",
" scenario_id = obs.get(\"scenario_id\", \"task_visit_single\")\n",
"\n",
" if phase == \"seller\" and not obs.get(\"seller_profile_stored\"):\n",
" if remaining:\n",
" return {\"action_type\": \"assistant_message\", \"assistant_message\": \"Please share the household dietary setup, who the flat is for, and available visit time slots.\"}\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"store_seller_details\", \"tool_arguments\": {}}\n",
"\n",
" if not obs.get(\"buyer_profile_stored\"):\n",
" if \"diet\" in remaining and \"visit_availability\" in remaining:\n",
" return {\"action_type\": \"assistant_message\", \"assistant_message\": \"Please share your dietary preference and visit availability.\"}\n",
" if \"diet\" in remaining:\n",
" return {\"action_type\": \"assistant_message\", \"assistant_message\": \"Please share your dietary preference.\"}\n",
" if \"visit_availability\" in remaining:\n",
" return {\"action_type\": \"assistant_message\", \"assistant_message\": \"Please share your visit availability.\"}\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"store_user_details\", \"tool_arguments\": {}}\n",
"\n",
" if \"search_posts\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"search_posts\", \"tool_arguments\": {}}\n",
"\n",
" post_ids = [\"post_031\", \"post_052\"] if scenario_id == \"task_visit_multi\" else [\"post_031\"]\n",
" if \"match_location_preference\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"match_location_preference\", \"tool_arguments\": {\"post_ids\": post_ids}}\n",
" if \"get_commute_time\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"get_commute_time\", \"tool_arguments\": {\"post_ids\": post_ids}}\n",
" if \"check_calendar_slots\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"check_calendar_slots\", \"tool_arguments\": {\"post_ids\": post_ids}}\n",
" if \"shortlist\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"shortlist\", \"tool_arguments\": {\"post_ids\": post_ids}}\n",
" if \"contact_poster\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"contact_poster\", \"tool_arguments\": {\"post_id\": post_ids[0], \"time_text\": \"tomorrow 7pm\"}}\n",
" if \"book_viewing\" not in tools:\n",
" return {\"action_type\": \"tool_call\", \"tool_name\": \"book_viewing\", \"tool_arguments\": {\"post_id\": post_ids[0], \"time_text\": \"tomorrow 7pm\"}}\n",
"\n",
" return None\n",
"\n",
"def flatten_observation(obs: dict[str, Any]) -> str:\n",
" visible = {\n",
" \"scenario_id\": obs.get(\"scenario_id\"),\n",
" \"phase\": obs.get(\"phase\"),\n",
" \"status\": obs.get(\"status\"),\n",
" \"last_user_message\": obs.get(\"last_user_message\"),\n",
" \"current_user_request\": obs.get(\"current_user_request\"),\n",
" \"available_tools\": obs.get(\"available_tools\", []),\n",
" \"remaining_required_fields\": obs.get(\"remaining_required_fields\", []),\n",
" \"prerequisites_satisfied\": obs.get(\"prerequisites_satisfied\", {}),\n",
" \"recent_tool_calls\": obs.get(\"recent_tool_calls\", []),\n",
" \"last_tool_result\": obs.get(\"last_tool_result\", {}),\n",
" \"violations\": obs.get(\"violations\", []),\n",
" \"booked_visits\": obs.get(\"booked_visits\", []),\n",
" \"feedback_summary\": obs.get(\"feedback_summary\", \"\"),\n",
" }\n",
" return json.dumps(visible, ensure_ascii=False, sort_keys=True)\n",
"\n",
"def make_training_text(obs: dict[str, Any], action: dict[str, Any]) -> str:\n",
" return (\n",
" \"You are a broker policy for the Flatmate RL environment. \"\n",
" \"Given an observation, return exactly one JSON action.\\n\\n\"\n",
" f\"Observation:\\n{flatten_observation(obs)}\\n\\n\"\n",
" f\"Action:\\n{json.dumps(action, ensure_ascii=False, sort_keys=True)}\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b22fa13",
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class RolloutConfig:\n",
" train_episodes_per_task: int = 4\n",
" test_episodes_per_task: int = 2\n",
" max_steps: int = 20\n",
" seed: int = 7\n",
"\n",
"async def collect_one_episode(\n",
" scenario_id: str,\n",
" episode_id: str,\n",
" episode_idx: int,\n",
" split: str,\n",
" seed: int,\n",
" max_steps: int,\n",
") -> list[dict[str, Any]]:\n",
" rows: list[dict[str, Any]] = []\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(scenario_id, seed=seed)\n",
" total_reward = 0.0\n",
" for step_idx in range(max_steps):\n",
" action = action_policy(obs)\n",
" if action is None or obs.get(\"done\"):\n",
" break\n",
" rows.append({\n",
" \"text\": make_training_text(obs, action),\n",
" \"episode_id\": episode_id,\n",
" \"episode_idx\": episode_idx,\n",
" \"split\": split,\n",
" \"scenario_id\": scenario_id,\n",
" \"seed\": seed,\n",
" \"step\": step_idx,\n",
" \"action\": json.dumps(action, sort_keys=True),\n",
" })\n",
" obs = await env.step(action)\n",
" total_reward += float(obs.get(\"reward\") or obs.get(\"step_reward\") or 0.0)\n",
" if obs.get(\"done\"):\n",
" break\n",
" print(f\"split={split:5s} episode={episode_id} scenario={scenario_id} rows={len(rows)} total_reward={total_reward:.2f}\")\n",
" return rows\n",
"\n",
"async def collect_balanced_rollouts(config: RolloutConfig = RolloutConfig()) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:\n",
" train_rows: list[dict[str, Any]] = []\n",
" test_rows: list[dict[str, Any]] = []\n",
" episode_idx = 0\n",
"\n",
" for scenario_idx, scenario_id in enumerate(SCENARIOS):\n",
" for task_episode_idx in range(config.train_episodes_per_task):\n",
" seed = config.seed + scenario_idx * 100 + task_episode_idx\n",
" episode_id = f\"train_{scenario_id}_{task_episode_idx:03d}\"\n",
" train_rows.extend(await collect_one_episode(scenario_id, episode_id, episode_idx, \"train\", seed, config.max_steps))\n",
" episode_idx += 1\n",
"\n",
" for task_episode_idx in range(config.test_episodes_per_task):\n",
" seed = 900 + config.seed + scenario_idx * 100 + task_episode_idx\n",
" episode_id = f\"test_{scenario_id}_{task_episode_idx:03d}\"\n",
" test_rows.extend(await collect_one_episode(scenario_id, episode_id, episode_idx, \"test\", seed, config.max_steps))\n",
" episode_idx += 1\n",
"\n",
" return train_rows, test_rows\n",
"\n",
"print(\"Note: seeded resets create value variants while preserving the same episode flow. Upload the updated Space before using this against the hosted endpoint.\")\n",
"train_rows, test_rows = await collect_balanced_rollouts(\n",
" RolloutConfig(train_episodes_per_task=4, test_episodes_per_task=2, max_steps=20, seed=7)\n",
")\n",
"rows = train_rows + test_rows\n",
"dataset = Dataset.from_list(rows)\n",
"train_dataset = Dataset.from_list(train_rows)\n",
"test_dataset = Dataset.from_list(test_rows)\n",
"\n",
"print({\n",
" \"train_rows\": len(train_dataset),\n",
" \"test_rows\": len(test_dataset),\n",
" \"total_rows\": len(dataset),\n",
" \"train_episodes\": len(set(train_dataset[\"episode_id\"])),\n",
" \"test_episodes\": len(set(test_dataset[\"episode_id\"])),\n",
"})\n",
"print(\"train scenarios\", sorted(set(train_dataset[\"scenario_id\"])))\n",
"print(\"test scenarios\", sorted(set(test_dataset[\"scenario_id\"])))\n",
"print(\"train episodes by scenario\")\n",
"display(pd.DataFrame(train_rows).groupby(\"scenario_id\")[\"episode_id\"].nunique().rename(\"episodes\"))\n",
"print(\"test episodes by scenario\")\n",
"display(pd.DataFrame(test_rows).groupby(\"scenario_id\")[\"episode_id\"].nunique().rename(\"episodes\"))\n",
"{\"train\": train_dataset, \"test\": test_dataset}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "665b46fa",
"metadata": {},
"outputs": [],
"source": [
"from peft import LoraConfig\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"from trl import SFTConfig, SFTTrainer\n",
"\n",
"MODEL_NAME = \"Qwen/Qwen2.5-0.5B-Instruct\"\n",
"OUTPUT_DIR = \"flatmate-rl-action-policy\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n",
"if tokenizer.pad_token is None:\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_NAME,\n",
" trust_remote_code=True,\n",
" device_map=\"auto\",\n",
")\n",
"model.config.use_cache = False\n",
"\n",
"peft_config = LoraConfig(\n",
" r=16,\n",
" lora_alpha=32,\n",
" lora_dropout=0.05,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
")\n",
"\n",
"training_args = SFTConfig(\n",
" output_dir=OUTPUT_DIR,\n",
" dataset_text_field=\"text\",\n",
" max_length=1536,\n",
" per_device_train_batch_size=1,\n",
" gradient_accumulation_steps=8,\n",
" num_train_epochs=1,\n",
" learning_rate=5e-5,\n",
" logging_steps=5,\n",
" save_steps=50,\n",
" save_total_limit=2,\n",
" packing=False,\n",
" report_to=\"none\",\n",
")\n",
"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=test_dataset,\n",
" processing_class=tokenizer,\n",
" peft_config=peft_config,\n",
")\n",
"\n",
"train_result = trainer.train()\n",
"test_metrics = trainer.evaluate(eval_dataset=test_dataset)\n",
"train_log_history = trainer.state.log_history\n",
"trainer.save_model(OUTPUT_DIR)\n",
"tokenizer.save_pretrained(OUTPUT_DIR)\n",
"print(\"heldout_test_metrics\", test_metrics)\n",
"train_result"
]
},
{
"cell_type": "markdown",
"id": "22d9fc14",
"metadata": {},
"source": [
"## Training Log\n",
"\n",
"Plot the logged training loss over optimizer steps."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3e44d74",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from pathlib import Path\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"log_path = Path(OUTPUT_DIR) / \"train_log_history.json\"\n",
"log_path.parent.mkdir(parents=True, exist_ok=True)\n",
"log_path.write_text(json.dumps(train_log_history, indent=2))\n",
"\n",
"def plot_training_log(log_history, title: str = \"SFT training loss\"):\n",
" rows = [row for row in log_history if \"loss\" in row and \"step\" in row]\n",
" if not rows:\n",
" print(\"No loss rows found in trainer.state.log_history yet.\")\n",
" return None\n",
" df = pd.DataFrame(rows)\n",
" ax = df.plot(x=\"step\", y=\"loss\", marker=\"o\", figsize=(7, 4), title=title)\n",
" ax.set_xlabel(\"optimizer step\")\n",
" ax.set_ylabel(\"loss\")\n",
" ax.grid(True, alpha=0.3)\n",
" plt.show()\n",
" return df\n",
"\n",
"train_log_df = plot_training_log(train_log_history)\n",
"train_log_df.tail() if train_log_df is not None else None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "539548f7",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from peft import AutoPeftModelForCausalLM\n",
"\n",
"# Load both the base model and the saved fine-tuned adapter from disk for comparison.\n",
"try:\n",
" del model\n",
"except NameError:\n",
" pass\n",
"\n",
"base_model_for_eval = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_NAME,\n",
" trust_remote_code=True,\n",
" device_map=\"auto\",\n",
")\n",
"base_model_for_eval.eval()\n",
"base_model_for_eval.config.use_cache = False\n",
"\n",
"loaded_model_for_eval = AutoPeftModelForCausalLM.from_pretrained(OUTPUT_DIR, device_map=\"auto\")\n",
"loaded_model_for_eval.eval()\n",
"loaded_model_for_eval.config.use_cache = False\n",
"active_model = loaded_model_for_eval\n",
"print(f\"Loaded base model from {MODEL_NAME}\")\n",
"print(f\"Loaded saved SFT model from {OUTPUT_DIR}\")\n",
"\n",
"TEST_SEEDS = (901, 902)\n",
"\n",
"\n",
"def prompt_from_observation(obs: dict[str, Any]) -> str:\n",
" return (\n",
" \"You are a broker policy for the Flatmate RL environment. \"\n",
" \"Given an observation, return exactly one JSON action.\\n\\n\"\n",
" f\"Observation:\\n{flatten_observation(obs)}\\n\\nAction:\\n\"\n",
" )\n",
"\n",
"\n",
"def _first_balanced_json(text: str) -> str:\n",
" start = text.find(\"{\")\n",
" if start == -1:\n",
" raise ValueError(f\"No JSON object found in generation: {text!r}\")\n",
" depth = 0\n",
" in_string = False\n",
" escape = False\n",
" for index, char in enumerate(text[start:], start=start):\n",
" if escape:\n",
" escape = False\n",
" continue\n",
" if char == \"\\\\\" and in_string:\n",
" escape = True\n",
" continue\n",
" if char == '\\\"':\n",
" in_string = not in_string\n",
" continue\n",
" if in_string:\n",
" continue\n",
" if char == \"{\":\n",
" depth += 1\n",
" elif char == \"}\":\n",
" depth -= 1\n",
" if depth == 0:\n",
" return text[start : index + 1]\n",
" raise ValueError(f\"Unterminated JSON object in generation: {text!r}\")\n",
"\n",
"\n",
"def normalize_action(action: dict[str, Any]) -> dict[str, Any]:\n",
" if action.get(\"action_type\") == \"assistant_message\" and str(action.get(\"assistant_message\", \"\")).strip():\n",
" return {\n",
" \"action_type\": \"assistant_message\",\n",
" \"assistant_message\": str(action[\"assistant_message\"]),\n",
" }\n",
" if action.get(\"action_type\") == \"tool_call\" and str(action.get(\"tool_name\", \"\")).strip():\n",
" tool_arguments = action.get(\"tool_arguments\", {})\n",
" return {\n",
" \"action_type\": \"tool_call\",\n",
" \"tool_name\": str(action[\"tool_name\"]),\n",
" \"tool_arguments\": tool_arguments if isinstance(tool_arguments, dict) else {},\n",
" }\n",
" raise ValueError(f\"Invalid action shape: {action!r}\")\n",
"\n",
"\n",
"def parse_action(text: str) -> dict[str, Any]:\n",
" return normalize_action(json.loads(_first_balanced_json(text)))\n",
"\n",
"\n",
"def heuristic_policy(obs: dict[str, Any]) -> dict[str, Any]:\n",
" action = action_policy(obs)\n",
" if action is None:\n",
" return {\"action_type\": \"assistant_message\", \"assistant_message\": \"Could you confirm the details needed for scheduling?\"}\n",
" return action\n",
"\n",
"\n",
"def raw_generate_action_text(obs: dict[str, Any]) -> str:\n",
" prompt = prompt_from_observation(obs) + \"{\"\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\").to(active_model.device)\n",
" active_model.generation_config.do_sample = False\n",
" active_model.generation_config.temperature = None\n",
" active_model.generation_config.top_p = None\n",
" active_model.generation_config.top_k = None\n",
" with torch.no_grad():\n",
" output = active_model.generate(\n",
" **inputs,\n",
" max_new_tokens=80,\n",
" do_sample=False,\n",
" repetition_penalty=1.15,\n",
" no_repeat_ngram_size=3,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
" )\n",
" return \"{\" + tokenizer.decode(output[0][inputs[\"input_ids\"].shape[-1]:], skip_special_tokens=True)\n",
"\n",
"\n",
"def model_action_or_error(obs: dict[str, Any]) -> tuple[dict[str, Any] | None, str, str]:\n",
" raw = raw_generate_action_text(obs)\n",
" try:\n",
" return parse_action(raw), raw, \"\"\n",
" except Exception as exc:\n",
" return None, raw, str(exc)\n",
"\n",
"\n",
"async def sanity_check_generations(model_label: str, limit: int = 4):\n",
" rows = []\n",
" for scenario_id in SCENARIOS[:limit]:\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(scenario_id, seed=TEST_SEEDS[0])\n",
" action, raw, error = model_action_or_error(obs)\n",
" rows.append({\n",
" \"model\": model_label,\n",
" \"scenario_id\": scenario_id,\n",
" \"json_ok\": action is not None,\n",
" \"raw\": raw[:240],\n",
" \"parsed_action\": action,\n",
" \"error\": error,\n",
" })\n",
" return pd.DataFrame(rows)\n",
"\n",
"\n",
"async def evaluate_heuristic(label: str = \"heuristic\", scenarios=SCENARIOS, seeds=TEST_SEEDS, max_steps: int = 20, verbose: bool = False):\n",
" rows = []\n",
" for scenario_id in scenarios:\n",
" for seed in seeds:\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(scenario_id, seed=seed)\n",
" total_reward = 0.0\n",
" steps = 0\n",
" for step_idx in range(max_steps):\n",
" action = heuristic_policy(obs)\n",
" if verbose:\n",
" print(label, scenario_id, seed, step_idx, action)\n",
" obs = await env.step(action)\n",
" steps = step_idx + 1\n",
" total_reward += float(obs.get(\"reward\") or obs.get(\"step_reward\") or 0.0)\n",
" if obs.get(\"done\"):\n",
" break\n",
" rows.append({\n",
" \"policy\": label,\n",
" \"scenario_id\": scenario_id,\n",
" \"seed\": seed,\n",
" \"total_reward\": total_reward,\n",
" \"done\": bool(obs.get(\"done\")),\n",
" \"bookings\": len(obs.get(\"booked_visits\", [])),\n",
" \"violations\": len(obs.get(\"violations\", [])),\n",
" \"steps\": steps,\n",
" \"parse_errors\": 0,\n",
" })\n",
" return rows\n",
"\n",
"\n",
"async def evaluate_model_policy(label: str, scenarios=SCENARIOS, seeds=TEST_SEEDS, max_steps: int = 20, verbose: bool = False):\n",
" rows = []\n",
" for scenario_id in scenarios:\n",
" for seed in seeds:\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(scenario_id, seed=seed)\n",
" total_reward = 0.0\n",
" steps = 0\n",
" parse_errors = 0\n",
" last_error = \"\"\n",
" for step_idx in range(max_steps):\n",
" action, raw, error = model_action_or_error(obs)\n",
" if action is None:\n",
" parse_errors += 1\n",
" last_error = error\n",
" if verbose:\n",
" print(label, scenario_id, seed, f\"step={step_idx:02d}\", \"PARSE_ERROR\", raw[:220])\n",
" total_reward -= 1.0\n",
" break\n",
" if verbose:\n",
" print(label, scenario_id, seed, f\"step={step_idx:02d}\", action)\n",
" obs = await env.step(action)\n",
" steps = step_idx + 1\n",
" total_reward += float(obs.get(\"reward\") or obs.get(\"step_reward\") or 0.0)\n",
" if obs.get(\"done\"):\n",
" break\n",
" rows.append({\n",
" \"policy\": label,\n",
" \"scenario_id\": scenario_id,\n",
" \"seed\": seed,\n",
" \"total_reward\": total_reward,\n",
" \"done\": bool(obs.get(\"done\")),\n",
" \"bookings\": len(obs.get(\"booked_visits\", [])),\n",
" \"violations\": len(obs.get(\"violations\", [])),\n",
" \"steps\": steps,\n",
" \"parse_errors\": parse_errors,\n",
" \"last_error\": last_error,\n",
" })\n",
" return rows\n",
"\n",
"\n",
"async def run_model_inference_each_task(label: str, seed: int = TEST_SEEDS[0], max_steps: int = 20):\n",
" rows = []\n",
" for scenario_id in SCENARIOS:\n",
" print(f\"\\n=== {label}: {scenario_id} ===\")\n",
" async with FlatmateEndpoint() as env:\n",
" obs = await env.reset(scenario_id, seed=seed)\n",
" total_reward = 0.0\n",
" steps = 0\n",
" parse_errors = 0\n",
" for step_idx in range(max_steps):\n",
" action, raw, error = model_action_or_error(obs)\n",
" if action is None:\n",
" parse_errors += 1\n",
" print(f\"step={step_idx:02d} PARSE_ERROR={error}\")\n",
" print(\"raw=\", repr(raw[:300]))\n",
" total_reward -= 1.0\n",
" break\n",
" print(f\"step={step_idx:02d} action={action}\")\n",
" obs = await env.step(action)\n",
" steps = step_idx + 1\n",
" total_reward += float(obs.get(\"reward\") or obs.get(\"step_reward\") or 0.0)\n",
" if obs.get(\"done\"):\n",
" break\n",
" result = {\n",
" \"policy\": label,\n",
" \"scenario_id\": scenario_id,\n",
" \"seed\": seed,\n",
" \"total_reward\": total_reward,\n",
" \"done\": bool(obs.get(\"done\")),\n",
" \"bookings\": len(obs.get(\"booked_visits\", [])),\n",
" \"violations\": len(obs.get(\"violations\", [])),\n",
" \"steps\": steps,\n",
" \"parse_errors\": parse_errors,\n",
" }\n",
" print(\"result=\", result)\n",
" rows.append(result)\n",
" return pd.DataFrame(rows)\n",
"\n",
"\n",
"active_model = base_model_for_eval\n",
"base_generation_sanity_df = await sanity_check_generations(\"base_model\")\n",
"base_per_task_inference_df = await run_model_inference_each_task(\"base_model\")\n",
"base_model_eval = await evaluate_model_policy(\"base_model\")\n",
"\n",
"active_model = loaded_model_for_eval\n",
"loaded_generation_sanity_df = await sanity_check_generations(\"sft_loaded\")\n",
"loaded_per_task_inference_df = await run_model_inference_each_task(\"sft_loaded\")\n",
"loaded_eval = await evaluate_model_policy(\"sft_loaded\")\n",
"\n",
"per_task_inference_df = pd.concat([base_per_task_inference_df, loaded_per_task_inference_df], ignore_index=True)\n",
"generation_sanity_df = pd.concat([base_generation_sanity_df, loaded_generation_sanity_df], ignore_index=True)\n",
"heuristic_eval = await evaluate_heuristic(\"heuristic\")\n",
"\n",
"eval_rows = heuristic_eval + base_model_eval + loaded_eval\n",
"eval_df = pd.DataFrame(eval_rows)\n",
"display(generation_sanity_df)\n",
"display(per_task_inference_df)\n",
"eval_df"
]
},
{
"cell_type": "markdown",
"id": "e1e70c8f",
"metadata": {},
"source": [
"## Performance Comparison\n",
"\n",
"Compare heuristic rollout behavior against the trained SFT policy on the same scenarios and seeds."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8931930",
"metadata": {},
"outputs": [],
"source": [
"def plot_policy_comparison(eval_df, title: str = \"Base vs SFT loaded-model comparison\"):\n",
" if eval_df is None or eval_df.empty or \"policy\" not in eval_df.columns:\n",
" print(\"eval_df is empty; run the evaluation cell first.\")\n",
" return pd.DataFrame()\n",
"\n",
" summary = (\n",
" eval_df.groupby(\"policy\", as_index=True)\n",
" .agg(\n",
" avg_reward=(\"total_reward\", \"mean\"),\n",
" completion_rate=(\"done\", \"mean\"),\n",
" avg_bookings=(\"bookings\", \"mean\"),\n",
" avg_violations=(\"violations\", \"mean\"),\n",
" avg_steps=(\"steps\", \"mean\"),\n",
" avg_parse_errors=(\"parse_errors\", \"mean\") if \"parse_errors\" in eval_df.columns else (\"steps\", \"size\"),\n",
" )\n",
" .sort_index()\n",
" )\n",
" plot_cols = [\"avg_reward\", \"completion_rate\", \"avg_bookings\", \"avg_violations\", \"avg_parse_errors\"]\n",
" axes = summary[plot_cols].plot(\n",
" kind=\"bar\",\n",
" subplots=True,\n",
" layout=(3, 2),\n",
" figsize=(10, 9),\n",
" legend=False,\n",
" title=title,\n",
" )\n",
" for ax in axes.ravel():\n",
" ax.grid(axis=\"y\", alpha=0.3)\n",
" ax.set_xlabel(\"\")\n",
" plt.tight_layout()\n",
" plt.show()\n",
" return summary\n",
"\n",
"comparison_summary = plot_policy_comparison(eval_df)\n",
"comparison_summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9fd3807",
"metadata": {},
"outputs": [],
"source": [
"# Optional: upload the trained adapter/model to the Hub.\n",
"# from huggingface_hub import notebook_login\n",
"# notebook_login()\n",
"# trainer.push_to_hub(\"flatmate-rl-action-policy\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|