Spaces:
Sleeping
Sleeping
| from typing import List, Any | |
| def _calculate_base_score(trajectory: List[dict]) -> float: | |
| if not trajectory: | |
| return 0.01 | |
| total_reward = sum(step.get("reward", 0.0) for step in trajectory) | |
| max_possible_reward = len(trajectory) * 1.0 | |
| if max_possible_reward == 0: | |
| return 0.01 | |
| score = total_reward / max_possible_reward | |
| # Clamp strictly between 0 and 1 — never exactly 0.0 or 1.0 | |
| return max(0.01, min(0.99, score)) | |
| def grade_easy(trajectory: List[dict], env_state: Any = None) -> float: | |
| score = _calculate_base_score(trajectory) | |
| # Threshold: 0.3 — cap at 0.95 to stay strictly below 1.0 | |
| if score >= 0.3: | |
| return 0.95 | |
| return max(0.01, min(0.99, score)) | |
| def grade_medium(trajectory: List[dict], env_state: Any = None) -> float: | |
| score = _calculate_base_score(trajectory) | |
| # Threshold: 0.6 — cap at 0.95 to stay strictly below 1.0 | |
| if score >= 0.6: | |
| return 0.95 | |
| return max(0.01, min(0.99, score)) | |
| def grade_hard(trajectory: List[dict], env_state: Any = None) -> float: | |
| score = _calculate_base_score(trajectory) | |
| # Threshold: 0.9 — cap at 0.95 to stay strictly below 1.0 | |
| if score >= 0.9: | |
| return 0.95 | |
| return max(0.01, min(0.99, score)) |