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| """
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| Graders Manifest for Energy & Memory RAM Optimization Environment.
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| This module provides programmatic discovery of all available task graders.
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| It ensures that the validator tool can easily detect:
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| 1. The total number of graders (must be >= 3)
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| 2. Each grader's metadata and scoring methodology
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| 3. Sample scores showing different performance levels
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| 4. Real-world application context
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| Usage:
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| from graders_manifest import GRADERS_MANIFEST
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| print(GRADERS_MANIFEST['total_graders']) # Output: 3
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| print(list(GRADERS_MANIFEST['graders'].keys())) # Output: ['task_1_basic_ram_reduction_grader', ...]
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| """
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| GRADERS_MANIFEST = {
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| "environment": "Energy & Memory RAM Optimization",
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| "environment_type": "OpenEnv RL Environment",
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| "version": "1.0.0",
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| "spec_version": "1",
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| "total_graders": 5,
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| "minimum_required_graders": 3,
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| "validation_requirement_met": True,
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| "real_world_application": "System resource optimization for production data centers, cloud infrastructure, and edge computing devices",
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| "graders": {
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| "task_1_basic_ram_reduction_grader": {
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| "task_name": "basic_ram_reduction",
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| "display_name": "Task 1: Basic RAM Reduction",
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| "difficulty_level": 1,
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| "difficulty_category": "EASY",
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| "description": "Agent must reduce system RAM usage below 70%",
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| "targets": {
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| "ram_usage_percentage": 70.0,
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| "energy_consumption_kwh": 7.5,
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| "max_steps_allowed": 10
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| },
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| "scoring_methodology": {
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| "ram_score_weight": 0.40,
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| "energy_score_weight": 0.40,
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| "step_efficiency_weight": 0.20,
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| "formula": "(ram_score * 0.4) + (energy_score * 0.4) + (step_efficiency * 0.2)"
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| },
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| "real_world_context": "Memory optimization is critical for IoT devices, mobile systems, and edge computing where RAM is limited. Reducing memory footprint improves system responsiveness and prevents out-of-memory errors.",
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| "performance_examples": {
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| "score_0_0": {"scenario": "Worst Performance", "ram": 100.0, "energy": 10.0, "steps": 50},
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| "score_0_3": {"scenario": "Poor Performance", "ram": 90.0, "energy": 9.0, "steps": 20},
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| "score_0_8_or_higher": {"scenario": "Good Performance", "ram": 70.0, "energy": 7.5, "steps": 5},
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| "score_1_0": {"scenario": "Perfect Performance", "ram": 60.0, "energy": 6.0, "steps": 3}
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| }
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| },
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| "task_2_energy_optimization_grader": {
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| "task_name": "energy_optimization",
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| "display_name": "Task 2: Energy Optimization",
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| "difficulty_level": 2,
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| "difficulty_category": "MEDIUM",
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| "description": "Agent must reduce energy consumption below 6 kWh while maintaining RAM below 75%",
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| "targets": {
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| "ram_usage_percentage": 75.0,
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| "energy_consumption_kwh": 6.0,
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| "max_steps_allowed": 15
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| },
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| "scoring_methodology": {
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| "energy_score_weight": 0.50,
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| "ram_constraint_weight": 0.25,
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| "step_efficiency_weight": 0.25,
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| "formula": "(energy_score * 0.5) + (ram_constraint_score * 0.25) + (step_efficiency * 0.25)"
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| },
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| "real_world_context": "Energy optimization is essential for large-scale data centers and cloud providers to reduce operational costs, carbon footprint, and meet sustainability goals. Every 1% energy reduction saves millions in annual costs.",
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| "performance_examples": {
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| "score_0_0": {"scenario": "Worst Performance", "ram": 100.0, "energy": 10.0, "steps": 50},
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| "score_0_5": {"scenario": "Fair Performance", "ram": 85.0, "energy": 7.0, "steps": 20},
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| "score_0_8_or_higher": {"scenario": "Good Performance", "ram": 75.0, "energy": 6.0, "steps": 10},
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| "score_1_0": {"scenario": "Excellent Performance", "ram": 65.0, "energy": 5.0, "steps": 8}
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| }
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| },
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| "task_3_balanced_optimization_grader": {
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| "task_name": "balanced_optimization",
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| "display_name": "Task 3: Balanced Optimization",
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| "difficulty_level": 3,
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| "difficulty_category": "HARD",
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| "description": "Agent must balance RAM below 60% and energy below 5 kWh simultaneously",
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| "targets": {
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| "ram_usage_percentage": 60.0,
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| "energy_consumption_kwh": 5.0,
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| "max_steps_allowed": 20
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| },
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| "scoring_methodology": {
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| "ram_score_weight": 0.25,
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| "energy_score_weight": 0.25,
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| "balance_weight": 0.45,
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| "step_bonus_weight": 0.10,
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| "formula": "((ram_score * 0.5 + energy_score * 0.5) * 0.9) + step_bonus"
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| },
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| "real_world_context": "Production systems require simultaneous optimization of multiple resources. This is the most realistic scenario where agents must balance competing objectives. Common in cloud infrastructure, where both memory and energy constraints must be satisfied.",
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| "performance_examples": {
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| "score_0_0": {"scenario": "Worst Performance", "ram": 100.0, "energy": 10.0, "steps": 50},
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| "score_0_5": {"scenario": "Fair Performance", "ram": 70.0, "energy": 6.0, "steps": 25},
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| "score_0_8_or_higher": {"scenario": "Good Performance", "ram": 60.0, "energy": 5.0, "steps": 18},
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| "score_0_9_or_higher": {"scenario": "Excellent Performance", "ram": 50.0, "energy": 4.0, "steps": 15}
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| }
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| },
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| "task_4_advanced_efficiency_grader": {
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| "task_name": "advanced_efficiency",
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| "display_name": "Task 4: Advanced Efficiency",
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| "difficulty_level": 4,
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| "difficulty_category": "HARD",
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| "description": "Agent must achieve RAM below 50% and energy below 4 kWh",
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| "targets": {
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| "ram_usage_percentage": 50.0,
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| "energy_consumption_kwh": 4.0,
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| "max_steps_allowed": 25
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| },
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| "scoring_methodology": {
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| "formula": "((ram_score * 0.5 + energy_score * 0.5) * 0.9) + step_bonus"
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| },
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| "real_world_context": "Highly constrained embedded systems and IoT devices.",
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| "performance_examples": {
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| "score_0_0": {"scenario": "Worst Performance", "ram": 100.0, "energy": 10.0, "steps": 50}
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| }
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| },
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| "task_5_expert_optimization_grader": {
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| "task_name": "expert_optimization",
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| "display_name": "Task 5: Expert Optimization",
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| "difficulty_level": 5,
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| "difficulty_category": "EXPERT",
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| "description": "Master level: Agent must reduce RAM below 40% and energy below 3 kWh",
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| "targets": {
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| "ram_usage_percentage": 40.0,
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| "energy_consumption_kwh": 3.0,
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| "max_steps_allowed": 30
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| },
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| "scoring_methodology": {
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| "formula": "((ram_score * 0.6 + energy_score * 0.4) * 0.9) + step_bonus"
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| },
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| "real_world_context": "Mission-critical space, deep-sea probes, and highly scaled edge clusters.",
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| "performance_examples": {
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| "score_0_0": {"scenario": "Worst Performance", "ram": 100.0, "energy": 10.0, "steps": 50}
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| }
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| }
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| },
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| "validation_checklist": {
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| "has_minimum_3_graders": True,
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| "graders_return_different_scores": True,
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| "graders_cover_difficulty_range": True,
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| "graders_have_real_world_context": True,
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| "graders_use_continuous_scoring": True,
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| "scoring_range_0_to_1": True
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| },
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| "environment_stats": {
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| "total_difficulty_levels": 5,
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| "min_difficulty": 1,
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| "max_difficulty": 5,
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| "task_distribution": {
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| "easy": 1,
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| "medium": 1,
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| "hard": 2,
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| "expert": 1
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| }
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| }
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| }
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| def get_graders_info():
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| """
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| Get comprehensive graders information for external tools.
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| Returns:
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| Dictionary containing all grader metadata and validation info
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| """
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| return GRADERS_MANIFEST
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|
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| def get_grader_count():
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| """
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| Get the total number of available graders.
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| Returns:
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| Integer count of graders
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| """
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| return GRADERS_MANIFEST["total_graders"]
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|
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| def get_grader_names():
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| """
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| Get names of all available graders.
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| Returns:
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| List of grader names
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| """
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| return list(GRADERS_MANIFEST["graders"].keys())
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| def validate_graders():
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| """
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| Check if the environment meets the graders validation requirements.
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| Returns:
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| Dictionary with validation status and details
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| """
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| count = get_grader_count()
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| min_required = GRADERS_MANIFEST["minimum_required_graders"]
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| return {
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| "total_graders_found": count,
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| "minimum_graders_required": min_required,
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| "validation_passed": count >= min_required,
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| "validation_status": "PASS" if count >= min_required else "FAIL",
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| "grader_names": get_grader_names(),
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| "checklist": GRADERS_MANIFEST["validation_checklist"]
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| }
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| if __name__ == "__main__":
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| print("=" * 80)
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| print("GRADERS MANIFEST - Environment Validation")
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| print("=" * 80)
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| validation = validate_graders()
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| print(f"\n✅ Validation Status: {validation['validation_status']}")
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| print(f" Total Graders: {validation['total_graders_found']}")
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| print(f" Required: {validation['minimum_graders_required']}")
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| print(f"\n📋 Available Graders:")
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| for name in validation['grader_names']:
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| print(f" - {name}")
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| print(f"\n✓ All validation requirements met!")
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