tahamajs/Sysmem2_in_AI/ComputerAssignments/CA24_multi_agent_systems - Storage Bucket

CA24: Advanced Multi-Agent Systems

🎯 Overview

A comprehensive implementation of advanced multi-agent systems featuring modern communication protocols, sophisticated coordination mechanisms, and multi-agent reinforcement learning. This project demonstrates state-of-the-art techniques in distributed AI systems.

✨ Key Features

1. Advanced Communication Systems

• 📨 Multiple communication protocols (Message Passing, Publish-Subscribe, Broadcast, Multicast)
• 🎯 Priority-based message handling (Low, Medium, High, Critical)
• 🔀 Intelligent message routing and filtering
• 📊 Real-time communication monitoring and statistics
• 🌐 WebSocket support for distributed agents
• ⏱️ TTL (Time-To-Live) and retry mechanisms

2. Advanced Coordination Mechanisms

• 🏗️ Hierarchical Coordination: Top-down task allocation
• 💰 Market-Based Allocation: Auction-based task distribution
• 🤝 Coalition Formation: Dynamic agent grouping for complex tasks
• 🎯 Consensus Algorithms: Weighted average and majority vote
• 📋 Task Allocation Methods:
  • Round Robin
  • Load Balancing
  • Capability Matching
  • Auction-Based
  • Genetic Algorithm

3. Multi-Agent Reinforcement Learning (MARL)

• 🧠 Multi-Agent DQN: Deep Q-Network for multiple agents
• 🎭 Multi-Agent A2C: Advantage Actor-Critic for cooperative learning
• 🔄 Experience Replay: Efficient learning from past experiences
• 🎮 Custom Environments: Flexible multi-agent simulation environments
• 📈 Training Metrics: Comprehensive performance tracking

4. Agent Types

• 👔 Coordinator Agent: Task allocation and system coordination
• 🔬 Researcher Agent: Information gathering and analysis
• 📊 Analyst Agent: Pattern recognition and prediction
• 🤖 ReAct Agent: Reasoning and acting with LLMs
• 🔮 Reflexion Agent: Self-reflective learning
• 🦛 HippoRAG Agent: Advanced RAG with knowledge graphs
• 🔗 Nexus Agent: Multi-modal reasoning

📁 Project Structure

CA24_multi_agent_systems/
├── 📓 notebooks/           # Jupyter notebooks with examples
│   ├── CA24.ipynb         # Main comprehensive notebook
│   ├── 01_Advanced_Communication_Systems.ipynb
│   ├── 02_Advanced_Coordination.ipynb
│   ├── 03_Multi_Agent_RL.ipynb
│   └── 04_Complete_Projects.ipynb
├── 🔧 src/                # Source code
│   ├── core/              # Core multi-agent systems
│   │   ├── multi_agent_system.py
│   │   ├── advanced_communication.py
│   │   ├── advanced_coordination.py
│   │   ├── multi_agent_rl.py
│   │   ├── environment.py
│   │   ├── rag_system.py
│   │   └── shared_memory.py
│   ├── agents/            # Specialized agents
│   │   ├── react_agent.py
│   │   ├── reflexion_agent.py
│   │   ├── hipporag_agent.py
│   │   └── nexus_agent.py
│   ├── evaluation/        # Evaluation metrics
│   └── utils/             # Utilities
├── 🚀 scripts/            # Execution scripts
│   ├── main.py
│   └── run.sh
├── 🧪 tests/              # Test files
├── ⚙️ config/             # Configuration
├── 📊 data/               # Data and results
├── 💾 models/             # Saved models
├── 🔗 integrations/       # External integrations
├── 🎮 demos/              # Interactive demos
└── 📋 docs/               # Documentation

🚀 Quick Start

Installation

# Clone the repository
cd CAs/CA24_multi_agent_systems

# Install dependencies
pip install -r requirements.txt

# Set up environment variables (optional)
export GEMINI_API_KEY='your_api_key_here'

Running Examples

1. Basic Multi-Agent System

cd scripts/
python main.py

2. Advanced Communication Demo

from core.advanced_communication import AdvancedCommunicationManager
from core.multi_agent_system import BaseAgent

# Create communication manager
comm_manager = AdvancedCommunicationManager("my_system")

# Register agents
await comm_manager.register_agent(agent1, ["topic_A"])
await comm_manager.register_agent(agent2, ["topic_B"])

# Send messages
await comm_manager.broadcast_message(
    sender="coordinator",
    content={"message": "Hello all agents!"},
    priority=MessagePriority.HIGH
)

3. Coordination with Coalition Formation

from core.advanced_coordination import AdvancedCoordinationManager, CoordinationStrategy

# Create coordination manager
coord_manager = AdvancedCoordinationManager(
    comm_manager,
    strategy=CoordinationStrategy.COALITION
)

# Register agents with capabilities
await coord_manager.register_agent(agent, capabilities)

# Allocate tasks
assigned_agent = await coord_manager.allocate_task(task)

4. Multi-Agent Reinforcement Learning

from core.multi_agent_rl import MultiAgentRLSystem

# Create MARL system
marl = MultiAgentRLSystem(
    num_agents=3,
    state_dim=4,
    action_dim=2,
    algorithm="dqn"
)

# Train agents
marl.train(num_episodes=1000, batch_size=32)

# Evaluate performance
results = marl.evaluate(num_episodes=10)

📚 Comprehensive Examples

Example 1: Distributed Task Allocation

import asyncio
from core.multi_agent_system import MultiAgentSystem
from core.advanced_coordination import CoordinationTask, AgentCapability

async def distributed_task_allocation():
    # Initialize system
    mas = MultiAgentSystem(gemini_config)
    await mas.initialize()

    # Create task with requirements
    task = CoordinationTask(
        description="Analyze market data",
        resource_requirements={
            "capabilities": ["analysis", "visualization"],
            "max_cost": 100.0
        },
        deadline=datetime.now() + timedelta(hours=1)
    )

    # Allocate task
    assigned_agent = await coord_manager.allocate_task(task)
    print(f"Task assigned to: {assigned_agent}")

    # Monitor progress
    status = await mas.get_system_status()
    print(f"System status: {status}")

asyncio.run(distributed_task_allocation())

Example 2: Consensus-Based Decision Making

async def consensus_decision():
    # Initial values from different agents
    initial_values = {
        "agent_1": 0.8,
        "agent_2": 0.6,
        "agent_3": 0.7,
        "agent_4": 0.9
    }

    # Reach consensus
    consensus_result = await coord_manager.reach_consensus(
        topic="resource_allocation",
        initial_values=initial_values,
        max_iterations=10
    )

    print(f"Consensus reached: {consensus_result}")

asyncio.run(consensus_decision())

Example 3: Multi-Agent RL Training

# Create cooperative multi-agent environment
marl_system = MultiAgentRLSystem(
    num_agents=4,
    state_dim=8,
    action_dim=4,
    algorithm="a2c"
)

# Train with custom parameters
marl_system.train(
    num_episodes=2000,
    batch_size=64,
    update_frequency=10
)

# Get training statistics
stats = marl_system.get_training_stats()
print(f"Training completed:")
print(f"  Average reward: {stats['average_rewards'][-1]:.2f}")
print(f"  Total steps: {stats['total_steps']}")

# Evaluate trained agents
eval_results = marl_system.evaluate(num_episodes=20)
print(f"Evaluation results: {eval_results}")

🎓 Jupyter Notebooks

Explore comprehensive tutorials and examples:

1. CA24.ipynb - Main comprehensive guide
2. 01_Advanced_Communication_Systems.ipynb - Communication protocols
3. 02_Advanced_Coordination.ipynb - Coordination mechanisms
4. 03_Multi_Agent_RL.ipynb - Reinforcement learning
5. 04_Complete_Projects.ipynb - End-to-end projects

🧪 Testing

# Run all tests
cd tests/
python -m pytest

# Run specific test
python test_communication.py
python test_coordination.py
python test_marl.py

📊 Performance Metrics

The system tracks various performance metrics:

• Communication: Message throughput, latency, delivery rate
• Coordination: Task allocation efficiency, coalition formation time
• Learning: Episode rewards, convergence rate, success rate
• System: CPU/memory usage, agent utilization

🔧 Configuration

Environment Variables

# API Keys
export GEMINI_API_KEY='your_gemini_key'
export OPENAI_API_KEY='your_openai_key'

# System Configuration
export MAX_AGENTS=10
export MESSAGE_TIMEOUT=5.0
export COORDINATION_STRATEGY='hierarchical'

Configuration Files

Edit config/system_config.yaml for advanced settings:

communication:
  protocol: message_passing
  max_retries: 3
  ttl: 60

coordination:
  strategy: hierarchical
  allocation_method: capability_matching

reinforcement_learning:
  algorithm: dqn
  learning_rate: 0.001
  gamma: 0.99
  epsilon: 1.0

📖 Documentation

Detailed documentation available in docs/:

• Architecture Guide: System design and components
• API Reference: Complete API documentation
• Best Practices: Guidelines for multi-agent systems
• Troubleshooting: Common issues and solutions

🎯 Key Concepts Implemented

1. Cooperative Distributed Problem Solving (CDPS)

• Decentralized control and data storage
• Efficient communication protocols
• Loose coupling between agents

2. Consensus Dynamics

• Weighted average consensus
• Majority vote mechanisms
• Convergence guarantees

3. Distributed Constraint Optimization (DCOP)

• Constraint satisfaction
• Resource allocation
• Optimization algorithms

4. Algorithmic Game Theory

• Mechanism design
• Equilibrium analysis
• Strategic behavior modeling

5. Multi-Agent Reinforcement Learning (MARL)

• Independent learners
• Centralized training, decentralized execution
• Cooperative and competitive scenarios

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📄 License

This project is part of the System2_in_AI CA collection.

🙏 Acknowledgments

• Research Papers: Based on latest MAS research
• Frameworks: LangChain, Ray RLlib, PettingZoo
• Community: Open-source multi-agent systems community

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