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Duration 28 hours
Course Outline
Foundations of Multi-Agent Systems
- Overview of agent types, environmental contexts, and interaction models
- Examining cooperation, competition, and autonomy within agentic systems
- Real-world applications in logistics, robotics, and strategic decision-making
Essential Agent Architecture Concepts
- Distinctions between reactive and deliberative agent models
- Communication protocols and various coordination frameworks
- Techniques for knowledge representation and managing shared state
Building Agents with Python
- Constructing agents using the Mesa framework
- Modeling environments and defining agent interactions
- Simulating agent behavior and generating visualizations
Coordination and Communication Mechanisms
- Architectures based on message passing and shared memory
- Strategies for negotiation, achieving consensus, and allocating tasks
- Application of coordination algorithms, including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Environments
- Applying reinforcement learning techniques to multi-agent scenarios
- Analyzing cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and System Scaling
- Utilizing Ray for running distributed multi-agent simulations
- Techniques for managing concurrency and ensuring synchronization
- Optimizing performance through parallelized computation and shared resource management
Human-Agent Collaboration
- Designing interfaces to support human-in-the-loop coordination
- Implementing hybrid workflows enhanced by AI-assisted decision support
- Addressing ethical implications and operational considerations
Capstone Project
- Designing and implementing a comprehensive multi-agent system in Python
- Demonstrating effective coordination and learning capabilities among agents
- Presenting simulation outcomes and key performance insights
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- Solid understanding of reinforcement learning or AI agent design principles
- Working knowledge of distributed systems and networking fundamentals
Target Audience
- System architects responsible for designing collaborative or distributed AI systems
- Researchers investigating coordination mechanisms and collective intelligence
- Engineers developing hybrid human-agent workflows or multi-agent solutions
