rustchain-community 1.0.0

// Agent Reinforcement Training - RULER Reward Calculation Module
use crate::core::Result;
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RewardMetrics {
    pub success_rate: f64,
    pub efficiency_score: f64,
    pub quality_score: f64,
    pub total_reward: f64,
}

impl Default for RewardMetrics {
    fn default() -> Self {
        Self {
            success_rate: 0.0,
            efficiency_score: 0.0,
            quality_score: 0.0,
            total_reward: 0.0,
        }
    }
}

pub fn calculate_reward(metrics: &RewardMetrics) -> Result<f64> {
    let reward = (metrics.success_rate * 0.5) 
        + (metrics.efficiency_score * 0.3) 
        + (metrics.quality_score * 0.2);
    Ok(reward.min(1.0).max(0.0))
}

pub fn update_reward_model(agent_id: &str, reward: f64) -> Result<()> {
    // Placeholder implementation for enterprise feature
    // In real implementation, this would update the agent's reward model
    tracing::info!("Updating reward model for agent {}: {}", agent_id, reward);
    Ok(())
}

pub fn evaluate_trajectory_quality(trajectory_data: &str) -> Result<f64> {
    // Placeholder implementation for enterprise feature
    // In real implementation, this would analyze trajectory quality
    let quality_score = if trajectory_data.len() > 100 { 0.8 } else { 0.4 };
    Ok(quality_score)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_calculate_reward() {
        let metrics = RewardMetrics {
            success_rate: 0.8,
            efficiency_score: 0.7,
            quality_score: 0.9,
            total_reward: 0.0,
        };
        let result = calculate_reward(&metrics);
        assert!(result.is_ok());
        let reward = result.unwrap();
        assert!(reward > 0.0 && reward <= 1.0);
        assert!((reward - 0.79).abs() < 0.01); // 0.8*0.5 + 0.7*0.3 + 0.9*0.2 = 0.79
    }

    #[test]
    fn test_update_reward_model() {
        let result = update_reward_model("test_agent", 0.75);
        assert!(result.is_ok());
    }

    #[test]
    fn test_evaluate_trajectory_quality() {
        let long_trajectory = "x".repeat(200);
        let short_trajectory = "short";
        
        let result1 = evaluate_trajectory_quality(&long_trajectory);
        assert!(result1.is_ok());
        assert_eq!(result1.unwrap(), 0.8);
        
        let result2 = evaluate_trajectory_quality(&short_trajectory);
        assert!(result2.is_ok());
        assert_eq!(result2.unwrap(), 0.4);
    }

    #[test]
    fn test_reward_metrics_default() {
        let metrics = RewardMetrics::default();
        assert_eq!(metrics.success_rate, 0.0);
        assert_eq!(metrics.efficiency_score, 0.0);
        assert_eq!(metrics.quality_score, 0.0);
        assert_eq!(metrics.total_reward, 0.0);
    }
}