'use client'; import React from 'react'; import { motion } from 'framer-motion'; import { Typography, Row, Col, Card, Badge } from 'antd'; import { BrainCircuitIcon, ThumbsUpIcon, ThumbsDownIcon, TrendingUpIcon, TargetIcon, FlaskConicalIcon, BarChart3Icon, Users2Icon, ClockIcon, SparklesIcon, AwardIcon, RefreshCwIcon } from 'lucide-react'; const { Title, Paragraph, Text } = Typography; // Animation variants const fadeInUp = { hidden: { opacity: 0, y: 30 }, visible: { opacity: 1, y: 0, transition: { duration: 0.6, ease: 'easeOut' } } }; const stagger = { visible: { transition: { staggerChildren: 0.15 } } }; const scaleOnHover = { hover: { scale: 1.02, transition: { duration: 0.2 } } }; export default function COOEEShowcase() { return (
{/* Header */}
COOEE Feedback & Learning (RL Context SLURP) Reinforcement learning for context relevance tuning with agent feedback collection, role-based filtering, and continuous improvement through real-world performance data.
{/* Main Features Grid */}
Reinforcement Learning Engine Advanced RL algorithms for context relevance optimization with multi-agent feedback integration and adaptive learning rates.
Reinforcement Learning Engine
Deep Q-Network implementation for context relevance optimization
Feedback Collection
Agent feedback via thumbs up/down, ratings, and detailed comments
Context Relevance Tuning
Dynamic scoring adjustments based on real-world performance data
Performance Analytics
Continuous monitoring and metrics collection for system improvement
Agent Feedback Collection Real-time feedback collection with upvote/downvote systems, detailed comments, and sentiment analysis for continuous improvement.
RESTful API for collecting structured feedback from agents Supports ratings, comments, and contextual relevance scoring
Machine learning models for context quality prediction Continuous training on agent feedback and performance metrics
Automated experimentation framework for optimization Statistical significance testing for context improvements
Live model updates based on feedback streams Immediate integration of new learning into context filtering
{/* Feedback System Components */}
RL Training Reinforcement learning from human feedback
Continuous Real-time learning and model updates
Analytics Performance tracking and improvement metrics
Multi-Agent Collaborative learning across agent networks
{/* Continuous Learning Features */} Continuous Improvement Through Real-World Data
Adaptive Learning Dynamic algorithm adjustment based on real-world performance metrics with personalized learning paths for different agent types.
A/B Testing Framework Automated experimentation platform for testing context relevance improvements with statistical significance validation.
Performance Rewards Incentive-based learning system with performance-based rewards and penalty mechanisms for optimal context quality.
{/* Role-Based Learning */} Role-Based Context Filtering & Access Control
Multi-Agent Learning Personalized learning models for different agent roles and capabilities
Performance Analytics Detailed metrics tracking and analysis for continuous optimization
Real-Time Adaptation Immediate learning integration with live performance monitoring
Predictive Modeling Future context relevance prediction based on historical patterns
); }