aitbc/tests/integration/test_advanced_agent_capabilities.py

#!/usr/bin/env python3
"""
Advanced Agent Capabilities Tests
Phase 9.1: Enhanced OpenClaw Agent Performance (Weeks 7-9)
"""

import pytest
import asyncio
import time
import json
import requests
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import logging
from enum import Enum

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class LearningAlgorithm(Enum):
    """Machine learning algorithms for agents"""
    Q_LEARNING = "q_learning"
    DEEP_Q_NETWORK = "deep_q_network"
    ACTOR_CRITIC = "actor_critic"
    PPO = "ppo"
    REINFORCE = "reinforce"
    SARSA = "sarsa"

class AgentCapability(Enum):
    """Advanced agent capabilities"""
    META_LEARNING = "meta_learning"
    SELF_OPTIMIZATION = "self_optimization"
    MULTIMODAL_FUSION = "multimodal_fusion"
    REINFORCEMENT_LEARNING = "reinforcement_learning"
    CREATIVITY = "creativity"
    SPECIALIZATION = "specialization"

@dataclass
class AgentSkill:
    """Agent skill definition"""
    skill_id: str
    skill_name: str
    skill_type: str
    proficiency_level: float
    learning_rate: float
    acquisition_date: datetime
    last_used: datetime
    usage_count: int
    
@dataclass
class LearningEnvironment:
    """Learning environment configuration"""
    environment_id: str
    environment_type: str
    state_space: Dict[str, Any]
    action_space: Dict[str, Any]
    reward_function: str
    constraints: List[str]
    
@dataclass
class ResourceAllocation:
    """Resource allocation for agents"""
    agent_id: str
    cpu_cores: int
    memory_gb: float
    gpu_memory_gb: float
    network_bandwidth_mbps: float
    storage_gb: float
    allocation_strategy: str

class AdvancedAgentCapabilitiesTests:
    """Test suite for advanced agent capabilities"""
    
    def __init__(self, agent_service_url: str = "http://127.0.0.1:8005"):
        self.agent_service_url = agent_service_url
        self.agents = self._setup_agents()
        self.skills = self._setup_skills()
        self.learning_environments = self._setup_learning_environments()
        self.session = requests.Session()
        self.session.timeout = 30
        
    def _setup_agents(self) -> List[Dict[str, Any]]:
        """Setup advanced agents for testing"""
        return [
            {
                "agent_id": "advanced_agent_001",
                "agent_type": "meta_learning_agent",
                "capabilities": [
                    AgentCapability.META_LEARNING,
                    AgentCapability.SELF_OPTIMIZATION,
                    AgentCapability.MULTIMODAL_FUSION
                ],
                "learning_algorithms": [
                    LearningAlgorithm.DEEP_Q_NETWORK,
                    LearningAlgorithm.ACTOR_CRITIC,
                    LearningAlgorithm.PPO
                ],
                "performance_metrics": {
                    "learning_speed": 0.85,
                    "adaptation_rate": 0.92,
                    "problem_solving": 0.88,
                    "creativity_score": 0.76
                },
                "resource_needs": {
                    "min_cpu_cores": 8,
                    "min_memory_gb": 16,
                    "min_gpu_memory_gb": 8,
                    "preferred_gpu_type": "nvidia_a100"
                }
            },
            {
                "agent_id": "creative_agent_001",
                "agent_type": "creative_specialist",
                "capabilities": [
                    AgentCapability.CREATIVITY,
                    AgentCapability.SPECIALIZATION,
                    AgentCapability.MULTIMODAL_FUSION
                ],
                "learning_algorithms": [
                    LearningAlgorithm.REINFORCE,
                    LearningAlgorithm.ACTOR_CRITIC
                ],
                "performance_metrics": {
                    "creativity_score": 0.94,
                    "innovation_rate": 0.87,
                    "specialization_depth": 0.91,
                    "cross_domain_application": 0.82
                },
                "resource_needs": {
                    "min_cpu_cores": 12,
                    "min_memory_gb": 32,
                    "min_gpu_memory_gb": 16,
                    "preferred_gpu_type": "nvidia_h100"
                }
            },
            {
                "agent_id": "optimization_agent_001",
                "agent_type": "resource_optimizer",
                "capabilities": [
                    AgentCapability.SELF_OPTIMIZATION,
                    AgentCapability.REINFORCEMENT_LEARNING
                ],
                "learning_algorithms": [
                    LearningAlgorithm.Q_LEARNING,
                    LearningAlgorithm.PPO,
                    LearningAlgorithm.SARSA
                ],
                "performance_metrics": {
                    "optimization_efficiency": 0.96,
                    "resource_utilization": 0.89,
                    "cost_reduction": 0.84,
                    "adaptation_speed": 0.91
                },
                "resource_needs": {
                    "min_cpu_cores": 6,
                    "min_memory_gb": 12,
                    "min_gpu_memory_gb": 4,
                    "preferred_gpu_type": "nvidia_a100"
                }
            }
        ]
        
    def _setup_skills(self) -> List[AgentSkill]:
        """Setup agent skills for testing"""
        return [
            AgentSkill(
                skill_id="multimodal_processing_001",
                skill_name="Advanced Multi-Modal Processing",
                skill_type="technical",
                proficiency_level=0.92,
                learning_rate=0.15,
                acquisition_date=datetime.now() - timedelta(days=30),
                last_used=datetime.now() - timedelta(hours=2),
                usage_count=145
            ),
            AgentSkill(
                skill_id="market_analysis_001",
                skill_name="Market Trend Analysis",
                skill_type="analytical",
                proficiency_level=0.87,
                learning_rate=0.12,
                acquisition_date=datetime.now() - timedelta(days=45),
                last_used=datetime.now() - timedelta(hours=6),
                usage_count=89
            ),
            AgentSkill(
                skill_id="creative_problem_solving_001",
                skill_name="Creative Problem Solving",
                skill_type="creative",
                proficiency_level=0.79,
                learning_rate=0.18,
                acquisition_date=datetime.now() - timedelta(days=20),
                last_used=datetime.now() - timedelta(hours=1),
                usage_count=34
            )
        ]
        
    def _setup_learning_environments(self) -> List[LearningEnvironment]:
        """Setup learning environments for testing"""
        return [
            LearningEnvironment(
                environment_id="marketplace_optimization_001",
                environment_type="reinforcement_learning",
                state_space={
                    "market_conditions": 10,
                    "agent_performance": 5,
                    "resource_availability": 8
                },
                action_space={
                    "pricing_adjustments": 5,
                    "resource_allocation": 7,
                    "strategy_selection": 4
                },
                reward_function="profit_maximization_with_constraints",
                constraints=["fair_trading", "resource_limits", "market_stability"]
            ),
            LearningEnvironment(
                environment_id="skill_acquisition_001",
                environment_type="meta_learning",
                state_space={
                    "current_skills": 20,
                    "learning_progress": 15,
                    "performance_history": 50
                },
                action_space={
                    "skill_selection": 25,
                    "learning_strategy": 6,
                    "resource_allocation": 8
                },
                reward_function="skill_acquisition_efficiency",
                constraints=["cognitive_load", "time_constraints", "resource_budget"]
            )
        ]
        
    async def test_meta_learning_capability(self, agent_id: str, learning_tasks: List[str]) -> Dict[str, Any]:
        """Test advanced meta-learning for faster skill acquisition"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            # Test meta-learning setup
            meta_learning_payload = {
                "agent_id": agent_id,
                "learning_tasks": learning_tasks,
                "meta_learning_algorithm": "MAML",  # Model-Agnostic Meta-Learning
                "adaptation_steps": 5,
                "meta_batch_size": 32,
                "inner_learning_rate": 0.01,
                "outer_learning_rate": 0.001
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/meta-learning/setup",
                json=meta_learning_payload,
                timeout=20
            )
            
            if response.status_code == 200:
                setup_result = response.json()
                
                # Test meta-learning training
                training_payload = {
                    "agent_id": agent_id,
                    "training_episodes": 100,
                    "task_distribution": "uniform",
                    "adaptation_evaluation": True
                }
                
                training_response = self.session.post(
                    f"{self.agent_service_url}/v1/meta-learning/train",
                    json=training_payload,
                    timeout=60
                )
                
                if training_response.status_code == 200:
                    training_result = training_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "learning_tasks": learning_tasks,
                        "setup_result": setup_result,
                        "training_result": training_result,
                        "adaptation_speed": training_result.get("adaptation_speed"),
                        "meta_learning_efficiency": training_result.get("efficiency"),
                        "skill_acquisition_rate": training_result.get("skill_acquisition_rate"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "setup_result": setup_result,
                        "training_error": f"Training failed with status {training_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"Meta-learning setup failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }
            
    async def test_self_optimizing_resource_management(self, agent_id: str, initial_allocation: ResourceAllocation) -> Dict[str, Any]:
        """Test self-optimizing agent resource management"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            # Test resource optimization setup
            optimization_payload = {
                "agent_id": agent_id,
                "initial_allocation": asdict(initial_allocation),
                "optimization_objectives": [
                    "minimize_cost",
                    "maximize_performance",
                    "balance_utilization"
                ],
                "optimization_algorithm": "reinforcement_learning",
                "optimization_horizon": "24h",
                "constraints": {
                    "max_cost_per_hour": 10.0,
                    "min_performance_threshold": 0.85,
                    "max_resource_waste": 0.15
                }
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/resource-optimization/setup",
                json=optimization_payload,
                timeout=15
            )
            
            if response.status_code == 200:
                setup_result = response.json()
                
                # Test optimization execution
                execution_payload = {
                    "agent_id": agent_id,
                    "optimization_period_hours": 24,
                    "performance_monitoring": True,
                    "auto_adjustment": True
                }
                
                execution_response = self.session.post(
                    f"{self.agent_service_url}/v1/resource-optimization/execute",
                    json=execution_payload,
                    timeout=30
                )
                
                if execution_response.status_code == 200:
                    execution_result = execution_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "initial_allocation": asdict(initial_allocation),
                        "optimized_allocation": execution_result.get("optimized_allocation"),
                        "cost_savings": execution_result.get("cost_savings"),
                        "performance_improvement": execution_result.get("performance_improvement"),
                        "resource_utilization": execution_result.get("resource_utilization"),
                        "optimization_efficiency": execution_result.get("efficiency"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "setup_result": setup_result,
                        "execution_error": f"Optimization execution failed with status {execution_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"Resource optimization setup failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }
            
    async def test_multimodal_agent_fusion(self, agent_id: str, modalities: List[str]) -> Dict[str, Any]:
        """Test multi-modal agent fusion for enhanced capabilities"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            # Test multimodal fusion setup
            fusion_payload = {
                "agent_id": agent_id,
                "input_modalities": modalities,
                "fusion_architecture": "cross_modal_attention",
                "fusion_strategy": "adaptive_weighting",
                "output_modalities": ["unified_representation"],
                "performance_targets": {
                    "fusion_accuracy": 0.90,
                    "processing_speed": 0.5,  # seconds
                    "memory_efficiency": 0.85
                }
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/multimodal-fusion/setup",
                json=fusion_payload,
                timeout=20
            )
            
            if response.status_code == 200:
                setup_result = response.json()
                
                # Test fusion processing
                processing_payload = {
                    "agent_id": agent_id,
                    "test_inputs": {
                        "text": "Analyze market trends for AI compute resources",
                        "image": "market_chart.png",
                        "audio": "market_analysis.wav",
                        "tabular": "price_data.csv"
                    },
                    "fusion_evaluation": True
                }
                
                processing_response = self.session.post(
                    f"{self.agent_service_url}/v1/multimodal-fusion/process",
                    json=processing_payload,
                    timeout=25
                )
                
                if processing_response.status_code == 200:
                    processing_result = processing_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "input_modalities": modalities,
                        "fusion_result": processing_result,
                        "fusion_accuracy": processing_result.get("accuracy"),
                        "processing_time": processing_result.get("processing_time"),
                        "memory_usage": processing_result.get("memory_usage"),
                        "cross_modal_attention_weights": processing_result.get("attention_weights"),
                        "enhanced_capabilities": processing_result.get("enhanced_capabilities"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "setup_result": setup_result,
                        "processing_error": f"Fusion processing failed with status {processing_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"Multimodal fusion setup failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }
            
    async def test_advanced_reinforcement_learning(self, agent_id: str, environment_id: str) -> Dict[str, Any]:
        """Test advanced reinforcement learning for marketplace strategies"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            environment = next((e for e in self.learning_environments if e.environment_id == environment_id), None)
            if not environment:
                return {"error": f"Environment {environment_id} not found"}
                
            # Test RL training setup
            rl_payload = {
                "agent_id": agent_id,
                "environment_id": environment_id,
                "algorithm": "PPO",  # Proximal Policy Optimization
                "hyperparameters": {
                    "learning_rate": 0.0003,
                    "batch_size": 64,
                    "gamma": 0.99,
                    "lambda": 0.95,
                    "clip_epsilon": 0.2,
                    "entropy_coefficient": 0.01
                },
                "training_episodes": 1000,
                "evaluation_frequency": 100,
                "convergence_threshold": 0.001
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/reinforcement-learning/train",
                json=rl_payload,
                timeout=120)  # 2 minutes for training
                
            if response.status_code == 200:
                training_result = response.json()
                
                # Test policy evaluation
                evaluation_payload = {
                    "agent_id": agent_id,
                    "environment_id": environment_id,
                    "evaluation_episodes": 100,
                    "deterministic_evaluation": True
                }
                
                evaluation_response = self.session.post(
                    f"{self.agent_service_url}/v1/reinforcement-learning/evaluate",
                    json=evaluation_payload,
                    timeout=30
                )
                
                if evaluation_response.status_code == 200:
                    evaluation_result = evaluation_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "environment_id": environment_id,
                        "training_result": training_result,
                        "evaluation_result": evaluation_result,
                        "convergence_episode": training_result.get("convergence_episode"),
                        "final_performance": evaluation_result.get("average_reward"),
                        "policy_stability": evaluation_result.get("policy_stability"),
                        "learning_curve": training_result.get("learning_curve"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "training_result": training_result,
                        "evaluation_error": f"Policy evaluation failed with status {evaluation_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"RL training failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }
            
    async def test_agent_creativity_development(self, agent_id: str, creative_challenges: List[str]) -> Dict[str, Any]:
        """Test agent creativity and specialized AI capability development"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            # Test creativity development setup
            creativity_payload = {
                "agent_id": agent_id,
                "creative_challenges": creative_challenges,
                "creativity_metrics": [
                    "novelty",
                    "usefulness",
                    "surprise",
                    "elegance",
                    "feasibility"
                ],
                "development_method": "generative_adversarial_learning",
                "inspiration_sources": [
                    "market_data",
                    "scientific_papers",
                    "art_patterns",
                    "natural_systems"
                ]
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/creativity/develop",
                json=creativity_payload,
                timeout=45
            )
            
            if response.status_code == 200:
                development_result = response.json()
                
                # Test creative problem solving
                problem_solving_payload = {
                    "agent_id": agent_id,
                    "problem_statement": "Design an innovative pricing strategy for AI compute resources that maximizes both provider earnings and consumer access",
                    "creativity_constraints": {
                        "market_viability": True,
                        "technical_feasibility": True,
                        "ethical_considerations": True
                    },
                    "solution_evaluation": True
                }
                
                solving_response = self.session.post(
                    f"{self.agent_service_url}/v1/creativity/solve",
                    json=problem_solving_payload,
                    timeout=30
                )
                
                if solving_response.status_code == 200:
                    solving_result = solving_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "creative_challenges": creative_challenges,
                        "development_result": development_result,
                        "problem_solving_result": solving_result,
                        "creativity_score": solving_result.get("creativity_score"),
                        "innovation_level": solving_result.get("innovation_level"),
                        "practical_applicability": solving_result.get("practical_applicability"),
                        "novel_solutions": solving_result.get("solutions"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "development_result": development_result,
                        "solving_error": f"Creative problem solving failed with status {solving_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"Creativity development failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }
            
    async def test_agent_specialization_development(self, agent_id: str, specialization_domain: str) -> Dict[str, Any]:
        """Test agent specialization in specific domains"""
        try:
            agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
            if not agent:
                return {"error": f"Agent {agent_id} not found"}
                
            # Test specialization development
            specialization_payload = {
                "agent_id": agent_id,
                "specialization_domain": specialization_domain,
                "training_data_sources": [
                    "domain_expert_knowledge",
                    "best_practices",
                    "case_studies",
                    "simulation_data"
                ],
                "specialization_depth": "expert",
                "cross_domain_transfer": True,
                "performance_targets": {
                    "domain_accuracy": 0.95,
                    "expertise_level": 0.90,
                    "adaptation_speed": 0.85
                }
            }
            
            response = self.session.post(
                f"{self.agent_service_url}/v1/specialization/develop",
                json=specialization_payload,
                timeout=60
            )
            
            if response.status_code == 200:
                development_result = response.json()
                
                # Test specialization performance
                performance_payload = {
                    "agent_id": agent_id,
                    "specialization_domain": specialization_domain,
                    "test_scenarios": 20,
                    "difficulty_levels": ["basic", "intermediate", "advanced", "expert"],
                    "performance_benchmark": True
                }
                
                performance_response = self.session.post(
                    f"{self.agent_service_url}/v1/specialization/evaluate",
                    json=performance_payload,
                    timeout=30
                )
                
                if performance_response.status_code == 200:
                    performance_result = performance_response.json()
                    
                    return {
                        "agent_id": agent_id,
                        "specialization_domain": specialization_domain,
                        "development_result": development_result,
                        "performance_result": performance_result,
                        "specialization_score": performance_result.get("specialization_score"),
                        "expertise_level": performance_result.get("expertise_level"),
                        "cross_domain_transferability": performance_result.get("cross_domain_transfer"),
                        "specialized_skills": performance_result.get("acquired_skills"),
                        "success": True
                    }
                else:
                    return {
                        "agent_id": agent_id,
                        "development_result": development_result,
                        "performance_error": f"Specialization evaluation failed with status {performance_response.status_code}",
                        "success": False
                    }
            else:
                return {
                    "agent_id": agent_id,
                    "error": f"Specialization development failed with status {response.status_code}",
                    "success": False
                }
                
        except Exception as e:
            return {
                "agent_id": agent_id,
                "error": str(e),
                "success": False
            }

# Test Fixtures
@pytest.fixture
async def advanced_agent_tests():
    """Create advanced agent capabilities test instance"""
    return AdvancedAgentCapabilitiesTests()

@pytest.fixture
def sample_resource_allocation():
    """Sample resource allocation for testing"""
    return ResourceAllocation(
        agent_id="advanced_agent_001",
        cpu_cores=8,
        memory_gb=16.0,
        gpu_memory_gb=8.0,
        network_bandwidth_mbps=1000,
        storage_gb=500,
        allocation_strategy="balanced"
    )

@pytest.fixture
def sample_learning_tasks():
    """Sample learning tasks for testing"""
    return [
        "market_price_prediction",
        "resource_demand_forecasting",
        "trading_strategy_optimization",
        "risk_assessment",
        "portfolio_management"
    ]

@pytest.fixture
def sample_modalities():
    """Sample modalities for multimodal fusion testing"""
    return ["text", "image", "audio", "tabular", "graph"]

@pytest.fixture
def sample_creative_challenges():
    """Sample creative challenges for testing"""
    return [
        "design_novel_marketplace_mechanism",
        "create_efficient_resource_allocation_algorithm",
        "develop_innovative_pricing_strategy",
        "solve_cold_start_problem_for_new_agents"
    ]

# Test Classes
class TestMetaLearningCapabilities:
    """Test advanced meta-learning capabilities"""
    
    @pytest.mark.asyncio
    async def test_meta_learning_setup(self, advanced_agent_tests, sample_learning_tasks):
        """Test meta-learning setup and configuration"""
        result = await advanced_agent_tests.test_meta_learning_capability(
            "advanced_agent_001",
            sample_learning_tasks
        )
        
        assert result.get("success", False), "Meta-learning setup failed"
        assert "setup_result" in result, "No setup result provided"
        assert "training_result" in result, "No training result provided"
        assert result.get("adaptation_speed", 0) > 0, "No adaptation speed measured"
        
    @pytest.mark.asyncio
    async def test_skill_acquisition_acceleration(self, advanced_agent_tests):
        """Test accelerated skill acquisition through meta-learning"""
        result = await advanced_agent_tests.test_meta_learning_capability(
            "advanced_agent_001",
            ["quick_skill_acquisition_test"]
        )
        
        assert result.get("success", False), "Skill acquisition test failed"
        assert result.get("skill_acquisition_rate", 0) > 0.5, "Skill acquisition rate too low"
        assert result.get("meta_learning_efficiency", 0) > 0.7, "Meta-learning efficiency too low"

class TestSelfOptimization:
    """Test self-optimizing resource management"""
    
    @pytest.mark.asyncio
    async def test_resource_optimization(self, advanced_agent_tests, sample_resource_allocation):
        """Test self-optimizing resource management"""
        result = await advanced_agent_tests.test_self_optimizing_resource_management(
            "optimization_agent_001",
            sample_resource_allocation
        )
        
        assert result.get("success", False), "Resource optimization test failed"
        assert "optimized_allocation" in result, "No optimized allocation provided"
        assert result.get("cost_savings", 0) > 0, "No cost savings achieved"
        assert result.get("performance_improvement", 0) > 0, "No performance improvement achieved"
        
    @pytest.mark.asyncio
    async def test_adaptive_resource_scaling(self, advanced_agent_tests):
        """Test adaptive resource scaling based on workload"""
        dynamic_allocation = ResourceAllocation(
            agent_id="optimization_agent_001",
            cpu_cores=4,
            memory_gb=8.0,
            gpu_memory_gb=4.0,
            network_bandwidth_mbps=500,
            storage_gb=250,
            allocation_strategy="dynamic"
        )
        
        result = await advanced_agent_tests.test_self_optimizing_resource_management(
            "optimization_agent_001",
            dynamic_allocation
        )
        
        assert result.get("success", False), "Adaptive scaling test failed"
        assert result.get("resource_utilization", 0) > 0.8, "Resource utilization too low"

class TestMultimodalFusion:
    """Test multi-modal agent fusion capabilities"""
    
    @pytest.mark.asyncio
    async def test_multimodal_fusion_setup(self, advanced_agent_tests, sample_modalities):
        """Test multi-modal fusion setup and processing"""
        result = await advanced_agent_tests.test_multimodal_agent_fusion(
            "advanced_agent_001",
            sample_modalities
        )
        
        assert result.get("success", False), "Multimodal fusion test failed"
        assert "fusion_result" in result, "No fusion result provided"
        assert result.get("fusion_accuracy", 0) > 0.85, "Fusion accuracy too low"
        assert result.get("processing_time", 10) < 1.0, "Processing time too slow"
        
    @pytest.mark.asyncio
    async def test_cross_modal_attention(self, advanced_agent_tests):
        """Test cross-modal attention mechanisms"""
        result = await advanced_agent_tests.test_multimodal_agent_fusion(
            "advanced_agent_001",
            ["text", "image", "audio"]
        )
        
        assert result.get("success", False), "Cross-modal attention test failed"
        assert "cross_modal_attention_weights" in result, "No attention weights provided"
        assert len(result.get("enhanced_capabilities", [])) > 0, "No enhanced capabilities detected"

class TestAdvancedReinforcementLearning:
    """Test advanced reinforcement learning for marketplace strategies"""
    
    @pytest.mark.asyncio
    async def test_ppo_training(self, advanced_agent_tests):
        """Test PPO reinforcement learning training"""
        result = await advanced_agent_tests.test_advanced_reinforcement_learning(
            "advanced_agent_001",
            "marketplace_optimization_001"
        )
        
        assert result.get("success", False), "PPO training test failed"
        assert "training_result" in result, "No training result provided"
        assert "evaluation_result" in result, "No evaluation result provided"
        assert result.get("final_performance", 0) > 0, "No positive final performance"
        assert result.get("convergence_episode", 1000) < 1000, "Training did not converge efficiently"
        
    @pytest.mark.asyncio
    async def test_policy_stability(self, advanced_agent_tests):
        """Test policy stability and consistency"""
        result = await advanced_agent_tests.test_advanced_reinforcement_learning(
            "advanced_agent_001",
            "marketplace_optimization_001"
        )
        
        assert result.get("success", False), "Policy stability test failed"
        assert result.get("policy_stability", 0) > 0.8, "Policy stability too low"
        assert "learning_curve" in result, "No learning curve provided"

class TestAgentCreativity:
    """Test agent creativity and innovation capabilities"""
    
    @pytest.mark.asyncio
    async def test_creativity_development(self, advanced_agent_tests, sample_creative_challenges):
        """Test creativity development and enhancement"""
        result = await advanced_agent_tests.test_agent_creativity_development(
            "creative_agent_001",
            sample_creative_challenges
        )
        
        assert result.get("success", False), "Creativity development test failed"
        assert "development_result" in result, "No creativity development result"
        assert "problem_solving_result" in result, "No creative problem solving result"
        assert result.get("creativity_score", 0) > 0.7, "Creativity score too low"
        assert result.get("innovation_level", 0) > 0.6, "Innovation level too low"
        
    @pytest.mark.asyncio
    async def test_novel_solution_generation(self, advanced_agent_tests):
        """Test generation of novel solutions"""
        result = await advanced_agent_tests.test_agent_creativity_development(
            "creative_agent_001",
            ["generate_novel_solution_test"]
        )
        
        assert result.get("success", False), "Novel solution generation test failed"
        assert len(result.get("novel_solutions", [])) > 0, "No novel solutions generated"
        assert result.get("practical_applicability", 0) > 0.5, "Solutions not practically applicable"

class TestAgentSpecialization:
    """Test agent specialization in specific domains"""
    
    @pytest.mark.asyncio
    async def test_domain_specialization(self, advanced_agent_tests):
        """Test agent specialization in specific domains"""
        result = await advanced_agent_tests.test_agent_specialization_development(
            "creative_agent_001",
            "marketplace_design"
        )
        
        assert result.get("success", False), "Domain specialization test failed"
        assert "development_result" in result, "No specialization development result"
        assert "performance_result" in result, "No specialization performance result"
        assert result.get("specialization_score", 0) > 0.8, "Specialization score too low"
        assert result.get("expertise_level", 0) > 0.7, "Expertise level too low"
        
    @pytest.mark.asyncio
    async def test_cross_domain_transfer(self, advanced_agent_tests):
        """Test cross-domain knowledge transfer"""
        result = await advanced_agent_tests.test_agent_specialization_development(
            "advanced_agent_001",
            "multi_domain_optimization"
        )
        
        assert result.get("success", False), "Cross-domain transfer test failed"
        assert result.get("cross_domain_transferability", 0) > 0.6, "Cross-domain transferability too low"
        assert len(result.get("specialized_skills", [])) > 0, "No specialized skills acquired"

if __name__ == "__main__":
    pytest.main([__file__, "-v", "--tb=short"])