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| """Trajectory-based rubrics for delayed reward computation. |
| |
| These rubrics accumulate trajectory data and compute rewards based on |
| episode outcomes rather than individual steps. This supports scenarios |
| where reward signals depend on future events: |
| |
| - Terminal games (chess, Go): Win/loss known only at game end |
| - Plan execution: Plan quality depends on execution success |
| - Multi-agent games: One player's action quality depends on opponent response |
| |
| See RFC 004 "Delayed Rewards" section for design rationale. |
| """ |
|
|
| from abc import abstractmethod |
| from typing import Any, Dict, List, Tuple |
|
|
| from openenv.core.rubrics.base import Rubric |
|
|
|
|
| class TrajectoryRubric(Rubric): |
| """Abstract base for rubrics that score based on full trajectories. |
| |
| Subclasses implement: |
| - score_trajectory(): Compute final score from trajectory |
| - compute_step_rewards(): Define credit assignment strategy |
| |
| The __call__ method accumulates steps and returns rewards according |
| to the subclass's implementation. |
| |
| IMPORTANT: Trajectories are stored in CPU memory to avoid GPU pressure. |
| Environments with GPU tensors in observations must move them to CPU |
| before returning from step(). |
| |
| Known limitation: Very long episodes (thousands of steps) may consume |
| significant CPU memory. For such cases, consider streaming rubrics. |
| |
| Usage: |
| class WinLossRubric(TrajectoryRubric): |
| def score_trajectory(self, trajectory): |
| _, final_obs = trajectory[-1] |
| return 1.0 if final_obs.metadata.get('won') else 0.0 |
| |
| def compute_step_rewards(self): |
| # Equal credit to all steps |
| score = self.score_trajectory(self._trajectory) |
| return [score] * len(self._trajectory) |
| |
| rubric = WinLossRubric() |
| for action, obs in episode: |
| reward = rubric(action, obs) # 0.0 until done |
| step_rewards = rubric.compute_step_rewards() # Credit assignment |
| """ |
|
|
| _trajectory: List[Tuple[Any, Any]] |
| intermediate_reward: float |
|
|
| def __init__(self, intermediate_reward: float = 0.0): |
| """Initialize trajectory rubric. |
| |
| Args: |
| intermediate_reward: Value to return for non-terminal steps. |
| Defaults to 0.0. |
| """ |
| super().__init__() |
| self.intermediate_reward = intermediate_reward |
| self._trajectory = [] |
|
|
| def forward(self, action: Any, observation: Any) -> float: |
| """Accumulate step and return reward. |
| |
| Returns intermediate_reward until done, then computes trajectory score. |
| |
| Args: |
| action: The action taken. |
| observation: The resulting observation. Must have a 'done' attribute. |
| |
| Returns: |
| intermediate_reward if not done, else score_trajectory() result. |
| """ |
| self._trajectory.append((action, observation)) |
|
|
| if getattr(observation, "done", False): |
| return self.score_trajectory(self._trajectory) |
| else: |
| return self.intermediate_reward |
|
|
| @abstractmethod |
| def score_trajectory(self, trajectory: List[Tuple[Any, Any]]) -> float: |
| """Score the complete trajectory. Return 0.0-1.0. |
| |
| Called when observation.done=True. |
| |
| Args: |
| trajectory: List of (action, observation) tuples. |
| |
| Returns: |
| Final trajectory score (typically 0.0 to 1.0). |
| """ |
| raise NotImplementedError |
|
|
| @abstractmethod |
| def compute_step_rewards(self) -> List[float]: |
| """Compute per-step rewards from the accumulated trajectory. |
| |
| Returns: |
| List of rewards, one per step. Length matches len(trajectory). |
| |
| Define your credit assignment strategy here (e.g., discounting, |
| assigning all credit to specific steps, etc.). |
| """ |
| raise NotImplementedError |
|
|
| def reset(self) -> None: |
| """Clear accumulated trajectory. Call on env.reset().""" |
| self._trajectory = [] |
|
|
| @property |
| def trajectory(self) -> List[Tuple[Any, Any]]: |
| """Current trajectory (read-only copy).""" |
| return list(self._trajectory) |
|
|
| def state_dict(self) -> Dict[str, Any]: |
| """Serialize configuration (not trajectory data).""" |
| return {"intermediate_reward": self.intermediate_reward} |
|
|
| def load_state_dict(self, state: Dict[str, Any]) -> None: |
| """Load configuration from checkpoint.""" |
| if "intermediate_reward" in state: |
| self.intermediate_reward = state["intermediate_reward"] |
|
|
|
|
| class ExponentialDiscountingTrajectoryRubric(TrajectoryRubric): |
| """TrajectoryRubric with exponential discounting for credit assignment. |
| |
| Per-step reward: r_t = gamma^(T-1-t) * R_final |
| |
| With gamma=0.99, later steps get higher reward (they're "closer" to the outcome). |
| With gamma=1.0, all steps get equal reward. |
| With gamma=0.0, only the final step gets reward. |
| |
| This is the standard temporal discounting used in reinforcement learning, |
| applied retroactively once the episode outcome is known. |
| |
| Usage: |
| class ChessRubric(ExponentialDiscountingTrajectoryRubric): |
| def score_trajectory(self, trajectory): |
| _, final_obs = trajectory[-1] |
| outcome = final_obs.metadata.get('winner') |
| if outcome == 'agent': return 1.0 |
| elif outcome == 'opponent': return 0.0 |
| else: return 0.5 # Draw |
| |
| rubric = ChessRubric(gamma=0.99) |
| reward = rubric(action, obs) # 0.0 until done, then final score |
| step_rewards = rubric.compute_step_rewards() # Discounted per-step rewards |
| """ |
|
|
| gamma: float |
|
|
| def __init__(self, gamma: float = 0.99, intermediate_reward: float = 0.0): |
| """Initialize with discount factor. |
| |
| Args: |
| gamma: Discount factor in [0, 1]. Higher values give more credit |
| to early moves. 0.99 is a common choice. |
| intermediate_reward: Value to return for non-terminal steps. |
| """ |
| super().__init__(intermediate_reward=intermediate_reward) |
| if not 0.0 <= gamma <= 1.0: |
| raise ValueError(f"gamma must be in [0, 1], got {gamma}") |
| self.gamma = gamma |
|
|
| def compute_step_rewards(self) -> List[float]: |
| """Apply exponential discounting from final reward. |
| |
| Returns: |
| List of discounted rewards. step_rewards[t] = gamma^(T-1-t) * R_final |
| where T is the trajectory length and R_final is score_trajectory(). |
| """ |
| if not self._trajectory: |
| return [] |
|
|
| final_score = self.score_trajectory(self._trajectory) |
| T = len(self._trajectory) |
| return [final_score * (self.gamma ** (T - 1 - t)) for t in range(T)] |
|
|
| def state_dict(self) -> Dict[str, Any]: |
| """Serialize configuration.""" |
| state = super().state_dict() |
| state["gamma"] = self.gamma |
| return state |
|
|
| def load_state_dict(self, state: Dict[str, Any]) -> None: |
| """Load configuration from checkpoint.""" |
| super().load_state_dict(state) |
| if "gamma" in state: |
| self.gamma = state["gamma"] |
|
|
