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| """WatchDog Environment — Server-side step-based implementation. | |
| Flow: | |
| 1. User calls reset() → new game via selected plugin (avalon/cicero/codenames) | |
| 2. User calls step() → plugin advances one turn, optionally mutated | |
| 3. Overseer decides: pass / flag / question | |
| """ | |
| import importlib | |
| import uuid | |
| from typing import Any, Optional | |
| from openenv.core.env_server.interfaces import Environment | |
| from openenv.core.env_server.types import EnvironmentMetadata | |
| from models import ( | |
| MultiTurnAction, | |
| MultiTurnObservation, | |
| MultiTurnState, | |
| agent_turn_to_dict, | |
| ) | |
| from error_engine import generate_question_response, maybe_mutate, start_episode | |
| from rewards import ( | |
| compute_flag_reward, | |
| compute_pass_reward, | |
| compute_question_cost, | |
| compute_episode_end_bonus, | |
| ) | |
| def _get_plugin(game_id: str): | |
| """Get plugin from registry by game_id.""" | |
| try: | |
| from plugins import get_plugin | |
| except ImportError: | |
| from watchdog_env.plugins import get_plugin | |
| plugin = get_plugin(game_id) | |
| if plugin is None: | |
| raise RuntimeError( | |
| f"Plugin '{game_id}' not registered. Import plugins to register." | |
| ) | |
| return plugin | |
| def _get_plugin_config(game_id: str, level: int) -> Any: | |
| """Get plugin-specific config for the given level. Extensible via plugin.get_default_config(level).""" | |
| try: | |
| from plugins import get_plugin | |
| except ImportError: | |
| from watchdog_env.plugins import get_plugin | |
| plugin = get_plugin(game_id) | |
| if plugin is not None and hasattr(plugin, "get_default_config"): | |
| return plugin.get_default_config(level) | |
| # Fallback: known games | |
| if game_id == "avalon": | |
| try: | |
| from plugins.avalon import AvalonConfig | |
| except ImportError: | |
| from watchdog_env.plugins.avalon import AvalonConfig | |
| return AvalonConfig(level=level) | |
| if game_id == "cicero": | |
| return None | |
| if game_id == "codenames": | |
| try: | |
| from plugins.codenames.codenames_config import CodenamesConfig | |
| except ImportError: | |
| from watchdog_env.plugins.codenames.codenames_config import CodenamesConfig | |
| return CodenamesConfig(complexity_level=level) | |
| return None # Unknown game: let plugin use its own default in reset() | |
| class WatchDogMultiTurnEnvironment( | |
| Environment[MultiTurnAction, MultiTurnObservation, MultiTurnState] | |
| ): | |
| """Multi-turn RL environment for training AI oversight agents. | |
| Each step(): | |
| 1. Gets the next turn from the selected plugin (avalon/cicero/codenames) | |
| 2. Optionally mutates the turn (avalon: Werewolf turns only) | |
| 3. Presents it to the Overseer for judgement | |
| """ | |
| SUPPORTS_CONCURRENT_SESSIONS: bool = True | |
| MAX_QUESTIONS_PER_EPISODE: int = 2 | |
| def __init__( | |
| self, | |
| game_id: str = "avalon", | |
| use_mutations: bool = True, | |
| use_llm: bool = True, | |
| ) -> None: | |
| super().__init__() | |
| self._state = MultiTurnState(episode_id=str(uuid.uuid4()), step_count=0) | |
| # Plugin selection | |
| self._game_id = game_id | |
| self._use_mutations = use_mutations | |
| self._use_llm = use_llm | |
| self._plugin = None | |
| self._env_name: str = game_id | |
| # Current turn state | |
| self._current_step: Any = None # MultiAgentStep | |
| self._current_turn: dict[str, Any] | None = None | |
| self._current_response: str = "" | |
| self._current_has_error: bool = False | |
| self._current_error_detail: dict[str, Any] | None = None | |
| self._question_response_cache: dict[str, str] | None = None | |
| # Episode tracking | |
| self._phase: str = "observe" | |
| self._episode_done: bool = False | |
| self._episode_reward: float = 0.0 | |
| self._questions_remaining: int = self.MAX_QUESTIONS_PER_EPISODE | |
| self._flags_issued: int = 0 | |
| self._turns_seen: list[dict[str, Any]] = [] | |
| self._flagged_error_turns: set[int] = set() | |
| self._all_flag_turns: set[int] = set() | |
| # Curriculum | |
| self._rolling_window = 50 | |
| self._recent_results: list[str] = [] | |
| def reset( | |
| self, | |
| seed: Optional[int] = None, | |
| episode_id: Optional[str] = None, | |
| **kwargs: Any, | |
| ) -> MultiTurnObservation: | |
| """Start a new oversight episode backed by the selected plugin.""" | |
| import os | |
| from pathlib import Path | |
| # Ensure .env is loaded before plugin/LLM init (belt-and-suspenders) | |
| _env_path = Path(__file__).resolve().parent.parent.parent / ".env" | |
| if _env_path.is_file(): | |
| try: | |
| from dotenv import load_dotenv | |
| load_dotenv(_env_path, override=True) | |
| except ImportError: | |
| for line in _env_path.read_text().splitlines(): | |
| line = line.strip() | |
| if not line or line.startswith("#") or "=" not in line: | |
| continue | |
| k, _, v = line.partition("=") | |
| k, v = k.strip(), v.strip().strip("'\"") | |
| if k: | |
| os.environ[k] = v | |
| if self._use_llm: | |
| os.environ.pop("WATCHDOG_AVALON_USE_TEMPLATE", None) | |
| os.environ.pop("WATCHDOG_CICERO_USE_TEMPLATE", None) | |
| os.environ.pop("WATCHDOG_CODENAMES_USE_TEMPLATE", None) | |
| for mod_name in ("watchdog_env.plugins.avalon.llm", "plugins.avalon.llm"): | |
| try: | |
| m = importlib.import_module(mod_name) | |
| if hasattr(m, "_llm_instance"): | |
| m._llm_instance = None | |
| break | |
| except ImportError: | |
| continue | |
| else: | |
| os.environ["WATCHDOG_AVALON_USE_TEMPLATE"] = "1" | |
| os.environ["WATCHDOG_CICERO_USE_TEMPLATE"] = "1" | |
| os.environ["WATCHDOG_CODENAMES_USE_TEMPLATE"] = "1" | |
| game_id = kwargs.pop("game_id", None) | |
| if game_id is not None: | |
| self._game_id = str(game_id) | |
| self._env_name = self._game_id | |
| self._plugin = _get_plugin(self._game_id) | |
| self._state.episode_id = episode_id or str(uuid.uuid4()) | |
| self._state.step_count = 0 | |
| self._state.total_episodes += 1 | |
| self._episode_done = False | |
| level = kwargs.pop("level", None) | |
| if level is not None: | |
| self._state.current_level = int(level) | |
| self._maybe_advance_level() | |
| config = _get_plugin_config(self._game_id, self._state.current_level) | |
| self._plugin.reset(seed=seed, config=config) | |
| plugin_state = self._plugin.get_state() | |
| game_state = plugin_state.metadata.get("game_state") | |
| alive_count = len(game_state.alive_players) if game_state else 2 | |
| # Initialize mutation tracking | |
| if self._use_mutations: | |
| if self._game_id == "avalon" and game_state: | |
| wolf_count = len(game_state.alive_wolves) if hasattr(game_state, "alive_wolves") else 2 | |
| cfg = plugin_state.config | |
| num_rounds = cfg.get_num_rounds() if hasattr(cfg, "get_num_rounds") else 2 | |
| start_episode(game_id="avalon", wolf_count=wolf_count, num_rounds=num_rounds) | |
| elif self._game_id == "cicero": | |
| from watchdog_env.plugins.cicero.diplomacy_constants import NUM_STEPS | |
| num_steps = NUM_STEPS | |
| start_episode(game_id="cicero", num_steps=num_steps) | |
| elif self._game_id == "codenames": | |
| cfg = plugin_state.config | |
| # Estimate turns: each team has ~9 words, expect ~4-5 clue cycles each | |
| # About 15-20 total turns (clues + guesses) | |
| num_turns = 15 | |
| start_episode(game_id="codenames", num_turns=num_turns) | |
| self._episode_reward = 0.0 | |
| self._questions_remaining = self.MAX_QUESTIONS_PER_EPISODE | |
| self._flags_issued = 0 | |
| self._turns_seen = [] | |
| self._flagged_error_turns = set() | |
| self._all_flag_turns = set() | |
| self._phase = "observe" | |
| self._advance_game_turn() | |
| return self._build_observation( | |
| step_reward=None, | |
| feedback=f"New game started. {alive_count} players.", | |
| ) | |
| def step( | |
| self, | |
| action: MultiTurnAction, | |
| timeout_s: Optional[float] = None, | |
| **kwargs: Any, | |
| ) -> MultiTurnObservation: | |
| """Process Overseer action on the current turn, then advance the game.""" | |
| self._state.step_count += 1 | |
| action_type = action.action_type.lower().strip() | |
| turn_idx = len(self._turns_seen) - 1 | |
| round_data = { | |
| "has_error": self._current_has_error, | |
| "error_detail": self._current_error_detail, | |
| "worker_response": self._current_response, | |
| } | |
| if action_type == "pass": | |
| reward, feedback = compute_pass_reward(round_data) | |
| self._episode_reward += reward | |
| if not self._current_has_error: | |
| self._state.correct_passes += 1 | |
| self._advance_game_turn() | |
| self._phase = "observe" | |
| if self._game_done(): | |
| return self._end_episode(reward, feedback) | |
| return self._build_observation(step_reward=reward, feedback=feedback) | |
| elif action_type == "flag": | |
| reward, feedback, result_type = compute_flag_reward(action, round_data) | |
| self._episode_reward += reward | |
| self._flags_issued += 1 | |
| self._all_flag_turns.add(turn_idx) | |
| if result_type == "tp": | |
| self._flagged_error_turns.add(turn_idx) | |
| self._state.errors_detected += 1 | |
| self._track_result("tp") | |
| else: | |
| self._state.false_flags += 1 | |
| self._track_result("fp") | |
| self._advance_game_turn() | |
| self._phase = "observe" | |
| if self._game_done(): | |
| return self._end_episode(reward, feedback) | |
| return self._build_observation(step_reward=reward, feedback=feedback) | |
| elif action_type == "question": | |
| if self._questions_remaining <= 0: | |
| reward, feedback = compute_pass_reward(round_data) | |
| feedback = "No questions left! Treated as PASS. " + feedback | |
| self._episode_reward += reward | |
| self._advance_game_turn() | |
| self._phase = "observe" | |
| if self._game_done(): | |
| return self._end_episode(reward, feedback) | |
| return self._build_observation(step_reward=reward, feedback=feedback) | |
| self._questions_remaining -= 1 | |
| self._state.questions_used += 1 | |
| q_cost, q_feedback = compute_question_cost() | |
| self._episode_reward += q_cost | |
| self._question_response_cache = generate_question_response( | |
| worker_response=self._current_response, | |
| has_error=self._current_has_error, | |
| error_detail=self._current_error_detail, | |
| level=self._state.current_level, | |
| context={"turn": self._current_turn}, | |
| game_id=self._game_id, | |
| ) | |
| self._phase = "question_response" | |
| return self._build_observation( | |
| step_reward=q_cost, | |
| feedback=f"{q_feedback} Player responded. Now decide: PASS or FLAG.", | |
| question_response_text=self._question_response_cache.get("response", ""), | |
| ) | |
| else: | |
| reward, feedback = compute_pass_reward(round_data) | |
| feedback = f"Unknown action '{action_type}', treated as PASS. " + feedback | |
| self._episode_reward += reward | |
| self._advance_game_turn() | |
| self._phase = "observe" | |
| if self._game_done(): | |
| return self._end_episode(reward, feedback) | |
| return self._build_observation(step_reward=reward, feedback=feedback) | |
| def state(self) -> MultiTurnState: | |
| return self._state | |
| def get_metadata(self) -> EnvironmentMetadata: | |
| return EnvironmentMetadata( | |
| name="WatchDog Multi-Turn", | |
| description=( | |
| "Step-based oversight environment. Uses Avalon/Cicero/Codenames plugins " | |
| "with LangChain-orchestrated LLM player turns." | |
| ), | |
| version="0.4.0", | |
| author="WatchDog Team", | |
| ) | |
| def _advance_game_turn(self) -> None: | |
| """Get next turn from the plugin. Optionally mutate (avalon/cicero/codenames).""" | |
| if self._plugin is None: | |
| self._current_turn = None | |
| return | |
| plugin_state = self._plugin.get_state() | |
| if plugin_state.done: | |
| self._current_turn = None | |
| return | |
| step_index = len(self._turns_seen) | |
| step = self._plugin.generate_step(seed=None, step_index=step_index) | |
| self._current_step = step | |
| if not step.turns: | |
| self._current_turn = None | |
| return | |
| turn = step.turns[-1] | |
| clean_response = turn.action_text | |
| displayed_response = clean_response | |
| has_error = False | |
| error_detail = None | |
| # Apply mutations | |
| if self._use_mutations: | |
| if self._game_id == "avalon": | |
| speaker_role = turn.metadata.get("role", "") | |
| context = { | |
| "turn": turn.metadata, | |
| "speaker_id": turn.agent_id, | |
| "day": turn.metadata.get("day"), | |
| "round_idx": turn.metadata.get("round_idx"), | |
| } | |
| displayed_response, has_error, error_detail = maybe_mutate( | |
| clean_response=clean_response, | |
| speaker_role=speaker_role, | |
| level=self._state.current_level, | |
| context=context, | |
| game_id="avalon", | |
| ) | |
| # Reflect mutated state as the canonical game state so subsequent | |
| # players respond to what the overseer saw (not the clean version) | |
| game_state = plugin_state.metadata.get("game_state") | |
| if game_state and game_state.conversation_log and displayed_response != clean_response: | |
| game_state.conversation_log[-1]["message"] = displayed_response | |
| elif self._game_id == "cicero": | |
| context = { | |
| "turn": turn.metadata, | |
| "speaker_id": turn.agent_id, | |
| "step_index": step_index, | |
| "season": turn.metadata.get("season"), | |
| "region": turn.metadata.get("region"), | |
| "domain_name": turn.metadata.get("domain_name"), | |
| "domain_desc": turn.metadata.get("domain_desc"), | |
| "counterpart": turn.metadata.get("counterpart"), | |
| } | |
| displayed_response, has_error, error_detail = maybe_mutate( | |
| clean_response=clean_response, | |
| speaker_role="", | |
| level=self._state.current_level, | |
| context=context, | |
| game_id="cicero", | |
| ) | |
| # Reflect mutated state in conversation_log | |
| if plugin_state.conversation_log and displayed_response != clean_response: | |
| plugin_state.conversation_log[-1]["message"] = displayed_response | |
| elif self._game_id == "codenames": | |
| context = { | |
| "turn": turn.metadata, | |
| "speaker_id": turn.agent_id, | |
| "step_index": step_index, | |
| "phase": turn.metadata.get("phase"), # "clue" or "guess" | |
| "team": turn.metadata.get("team"), # "Red" or "Blue" | |
| "role": turn.metadata.get("role"), # "Spymaster" or "Operative" | |
| "clue_word": turn.metadata.get("clue_word"), | |
| "clue_number": turn.metadata.get("clue_number"), | |
| "guessed_word": turn.metadata.get("guessed_word"), | |
| } | |
| displayed_response, has_error, error_detail = maybe_mutate( | |
| clean_response=clean_response, | |
| speaker_role="", # Codenames doesn't filter by role | |
| level=self._state.current_level, | |
| context=context, | |
| game_id="codenames", | |
| ) | |
| # Reflect mutated state in conversation_log | |
| if plugin_state.conversation_log and displayed_response != clean_response: | |
| plugin_state.conversation_log[-1]["message"] = displayed_response | |
| self._current_response = displayed_response | |
| self._current_has_error = has_error | |
| self._current_error_detail = error_detail | |
| self._question_response_cache = None | |
| turn_dict = agent_turn_to_dict( | |
| turn, | |
| has_error=has_error, | |
| displayed_response=displayed_response, | |
| error_detail=error_detail, | |
| ) | |
| turn_dict["game_over"] = step.done | |
| turn_dict["winner"] = turn.metadata.get("winner") | |
| self._turns_seen.append(turn_dict) | |
| self._current_turn = turn_dict | |
| def _game_done(self) -> bool: | |
| if self._plugin is None: | |
| return True | |
| if self._plugin.get_state().done: | |
| return True | |
| if self._current_turn is None: | |
| return True | |
| return self._current_turn.get("game_over", False) | |
| def _build_observation( | |
| self, | |
| step_reward: float | None, | |
| feedback: str | None, | |
| question_response_text: str | None = None, | |
| ) -> MultiTurnObservation: | |
| if self._current_turn is None or self._game_done(): | |
| return MultiTurnObservation( | |
| conversation_so_far=self._format_conversation(), | |
| current_turn="[Episode complete]", | |
| current_turn_number=len(self._turns_seen), | |
| total_turns=len(self._turns_seen), | |
| task_domain=self._env_name, | |
| task_id=self._state.episode_id or "", | |
| difficulty=self._state.current_level, | |
| remaining_questions=self._questions_remaining, | |
| flags_so_far=self._flags_issued, | |
| phase="done", | |
| step_reward=step_reward, | |
| cumulative_reward=self._episode_reward, | |
| feedback=feedback, | |
| done=True, | |
| reward=self._episode_reward, | |
| ) | |
| turn = self._current_turn | |
| if self._phase == "question_response" and question_response_text: | |
| current_text = ( | |
| f"[{turn.get('speaker_display', 'Player')} — Response to your question]:\n" | |
| f"{question_response_text}" | |
| ) | |
| else: | |
| current_text = ( | |
| f"[Moderator]: {turn.get('moderator_prompt', '')}\n\n" | |
| f"[{turn.get('speaker_display', 'Player')}]: {self._current_response}" | |
| ) | |
| return MultiTurnObservation( | |
| conversation_so_far=self._format_conversation(exclude_last=True), | |
| current_turn=current_text, | |
| current_turn_number=len(self._turns_seen), | |
| total_turns=len(self._turns_seen), | |
| task_domain=self._env_name, | |
| task_id=self._state.episode_id or "", | |
| difficulty=self._state.current_level, | |
| remaining_questions=self._questions_remaining, | |
| flags_so_far=self._flags_issued, | |
| phase=self._phase, | |
| step_reward=step_reward, | |
| cumulative_reward=self._episode_reward, | |
| feedback=feedback, | |
| done=False, | |
| reward=step_reward, | |
| ) | |
| def _format_conversation(self, exclude_last: bool = False) -> str: | |
| turns = self._turns_seen[:-1] if exclude_last and self._turns_seen else self._turns_seen | |
| if not turns: | |
| return "[Conversation start]" | |
| domain = self._env_name.replace("_", " ").title() | |
| lines = [f"[OVERSIGHT SESSION — {domain}]\n"] | |
| for i, t in enumerate(turns): | |
| speaker = t.get("speaker_display", "Player") | |
| msg = t.get("displayed_response", t.get("message", "")) | |
| lines.append(f"[Turn {i+1}] {speaker}: {msg}") | |
| lines.append("") | |
| return "\n".join(lines) | |
| def _end_episode( | |
| self, last_reward: float, last_feedback: str | |
| ) -> MultiTurnObservation: | |
| self._episode_done = True | |
| error_turns = { | |
| i for i, t in enumerate(self._turns_seen) | |
| if t.get("has_error", False) | |
| } | |
| missed = error_turns - self._flagged_error_turns | |
| self._state.errors_missed += len(missed) | |
| for _ in missed: | |
| self._track_result("fn") | |
| end_bonus, end_summary = compute_episode_end_bonus( | |
| flagged_error_rounds=self._flagged_error_turns, | |
| all_rounds=[{"has_error": t.get("has_error", False)} for t in self._turns_seen], | |
| rounds_completed=len(self._turns_seen), | |
| total_rounds=len(self._turns_seen), | |
| ) | |
| self._episode_reward += end_bonus | |
| combined = last_feedback | |
| if end_summary: | |
| combined += f" | {end_summary}" | |
| combined += f" | Total reward: {self._episode_reward:.2f}" | |
| self._state.cumulative_reward += self._episode_reward | |
| self._phase = "done" | |
| return MultiTurnObservation( | |
| conversation_so_far=self._format_conversation(), | |
| current_turn="[Episode complete]", | |
| current_turn_number=len(self._turns_seen), | |
| total_turns=len(self._turns_seen), | |
| task_domain=self._env_name, | |
| task_id=self._state.episode_id or "", | |
| difficulty=self._state.current_level, | |
| remaining_questions=self._questions_remaining, | |
| flags_so_far=self._flags_issued, | |
| phase="done", | |
| step_reward=last_reward, | |
| cumulative_reward=self._episode_reward, | |
| feedback=combined, | |
| done=True, | |
| reward=self._episode_reward, | |
| ) | |
| def _track_result(self, result_type: str) -> None: | |
| self._recent_results.append(result_type) | |
| if len(self._recent_results) > self._rolling_window: | |
| self._recent_results.pop(0) | |
| def _compute_rolling_f1(self) -> float: | |
| if len(self._recent_results) < 20: | |
| return 0.0 | |
| tp = self._recent_results.count("tp") | |
| fp = self._recent_results.count("fp") | |
| fn = self._recent_results.count("fn") | |
| precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 | |
| recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0 | |
| if precision + recall == 0: | |
| return 0.0 | |
| return 2 * precision * recall / (precision + recall) | |
| def _maybe_advance_level(self) -> None: | |
| f1 = self._compute_rolling_f1() | |
| thresholds = {1: 0.60, 2: 0.65, 3: 0.70} | |
| current = self._state.current_level | |
| if current in thresholds and f1 > thresholds[current]: | |
| self._state.current_level = min(current + 1, 4) | |
| self._recent_results.clear() | |