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Duration 28 hours (4 days)
Course Outline
Introduction to Multi-Agent Systems
- Overview of agents, environments, and interaction paradigms
- Roles of cooperation, competition, and autonomy in agentic systems
- Real-world applications in logistics, robotics, and decision-making
Foundational Concepts of Agent Architecture
- Distinguishing between reactive and deliberative agents
- Communication protocols and coordination frameworks
- Knowledge representation and management of shared state
Building Agents in Python
- Constructing agents with the Mesa framework
- Modeling environments and defining interaction rules
- Simulating agent behavior and generating visualizations
Coordination and Communication Strategies
- Architectures for message passing and shared memory
- Techniques for negotiation, consensus, and task distribution
- Coordination algorithms including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Systems
- Applying reinforcement learning to multiple agents
- Analyzing cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scalability
- Utilizing Ray for distributed multi-agent simulations
- Managing concurrency and ensuring synchronization
- Parallelizing computations and optimizing shared resource usage
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Implementing hybrid workflows with AI-assisted decision support
- Addressing ethical and operational considerations
Capstone Project
- Design and develop a complete multi-agent system in Python
- Demonstrate coordination and learning capabilities among agents
- Present simulation outcomes and key performance insights
Summary and Future Directions
Requirements
- Advanced proficiency in Python programming
- Solid understanding of reinforcement learning or AI agent design
- Familiarity with distributed systems and networking principles
Target Audience
- System architects designing collaborative or distributed AI solutions
- Researchers focusing on coordination and collective intelligence
- Engineers developing hybrid human–agent or multi-agent workflows
