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models.py

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+from pydantic import BaseModel, Field
+from typing import Optional, List, Dict, Any
+from enum import Enum
+
+
+class TaskDifficulty(str, Enum):
+    EASY = "easy"
+    MEDIUM = "medium"
+    HARD = "hard"
+
+
+class ActionType(str, Enum):
+    PREDICT_CONFUSION = "predict_confusion"
+    ANALYZE_BEHAVIOR = "analyze_behavior"
+    TRIGGER_INTERVENTION = "trigger_intervention"
+    CLASSIFY_DIFFICULTY = "classify_difficulty"
+    FUSE_MODALITIES = "fuse_modalities"
+
+
+class Observation(BaseModel):
+    step: int = Field(..., description="Current step in the episode")
+    episode_id: str = Field(..., description="Unique episode identifier")
+    
+    learning_context: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Current learning context (topic, difficulty, time spent)"
+    )
+    
+    learner_state: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Learner state signals from all modalities"
+    )
+    
+    gaze_features: List[float] = Field(
+        default_factory=list,
+        description="Gaze tracking features (16 dimensions)"
+    )
+    
+    gesture_features: List[float] = Field(
+        default_factory=list,
+        description="Hand gesture features (21 landmarks x 3 coords)"
+    )
+    
+    biometric_features: List[float] = Field(
+        default_factory=list,
+        description="Biometric features (heart rate, GSR, etc.)"
+    )
+    
+    audio_features: List[float] = Field(
+        default_factory=list,
+        description="Audio features (pitch, tone, pauses)"
+    )
+    
+    behavioral_features: List[float] = Field(
+        default_factory=list,
+        description="Behavioral features (scroll speed, clicks, typing)"
+    )
+    
+    confusion_history: List[float] = Field(
+        default_factory=list,
+        description="Historical confusion probabilities"
+    )
+    
+    prediction_window: int = Field(
+        default=5,
+        description="Steps ahead to predict confusion"
+    )
+    
+    available_interventions: List[str] = Field(
+        default_factory=list,
+        description="Available intervention types"
+    )
+    
+    multimodal_fused: bool = Field(
+        default=False,
+        description="Whether multi-modal fusion is enabled"
+    )
+    
+    metadata: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional metadata"
+    )
+
+
+class Action(BaseModel):
+    action_type: ActionType = Field(..., description="Type of action to take")
+    
+    predicted_confusion: Optional[float] = Field(
+        None,
+        description="Predicted confusion probability (0.0-1.0)",
+        ge=0.0,
+        le=1.0
+    )
+    
+    intervention_type: Optional[str] = Field(
+        None,
+        description="Intervention to trigger (if action_type is trigger_intervention)"
+    )
+    
+    intervention_intensity: Optional[float] = Field(
+        None,
+        description="Intervention intensity (0.0-1.0)",
+        ge=0.0,
+        le=1.0
+    )
+    
+    difficulty_prediction: Optional[TaskDifficulty] = Field(
+        None,
+        description="Predicted task difficulty (if action_type is classify_difficulty)"
+    )
+    
+    modality_weights: Optional[Dict[str, float]] = Field(
+        None,
+        description="Weights for multi-modal fusion",
+        ge=0.0,
+        le=1.0
+    )
+    
+    reasoning: Optional[str] = Field(
+        None,
+        description="Agent's reasoning for the action"
+    )
+
+
+class Reward(BaseModel):
+    total: float = Field(..., description="Total reward for this step")
+    
+    confusion_prediction_reward: float = Field(
+        default=0.0,
+        description="Reward for confusion prediction accuracy"
+    )
+    
+    early_detection_reward: float = Field(
+        default=0.0,
+        description="Reward for early confusion detection"
+    )
+    
+    intervention_reward: float = Field(
+        default=0.0,
+        description="Reward for effective intervention"
+    )
+    
+    partial_progress_reward: float = Field(
+        default=0.0,
+        description="Reward for partial progress toward goals"
+    )
+    
+    penalty: float = Field(
+        default=0.0,
+        description="Penalty for negative behaviors"
+    )
+    
+    metadata: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional reward metadata"
+    )
+
+
+class State(BaseModel):
+    episode_id: str = Field(..., description="Unique episode identifier")
+    step_count: int = Field(default=0, description="Number of steps taken")
+    max_steps: int = Field(default=100, description="Maximum steps per episode")
+    task_difficulty: TaskDifficulty = Field(default=TaskDifficulty.MEDIUM)
+    ground_truth_confusion: Optional[float] = Field(None, description="Actual confusion level")
+    predictions_history: List[Dict[str, Any]] = Field(default_factory=list)
+    interventions_history: List[Dict[str, Any]] = Field(default_factory=list)
+    episode_reward: float = Field(default=0.0)
+    task_complete: bool = Field(default=False)
+    task_success: bool = Field(False)
+
+
+class StepResult(BaseModel):
+    observation: Observation
+    reward: Reward
+    done: bool
+    info: Dict[str, Any] = Field(default_factory=dict)
+
+
+class GraderResult(BaseModel):
+    score: float = Field(..., ge=0.0, le=1.0, description="Grader score (0.0-1.0)")
+    feedback: str = Field(..., description="Feedback on performance")
+    metrics: Dict[str, float] = Field(default_factory=dict)
+    passed: bool = Field(..., description="Whether task passed")
+
+
+__all__ = [
+    "Observation",
+    "Action",
+    "Reward",
+    "State",
+    "StepResult",
+    "GraderResult",
+    "TaskDifficulty",
+    "ActionType",
+]