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Duration 28 hours
Course Outline
Introduction to Multi-Agent Systems
- Fundamentals of agents, environments, and interaction models
- Dynamics of cooperation, competition, and autonomy in agentic systems
- Real-world applications in logistics, robotics, and decision support
Foundational Concepts of Agent Architecture
- Differentiating reactive versus deliberative agents
- Communication protocols and coordination frameworks
- Knowledge representation techniques and shared state management
Agent Implementation in Python
- Constructing agents with the Mesa framework
- Modeling environmental factors and agent interactions
- Simulating agent behaviors and generating visual outputs
Coordination and Communication Mechanisms
- Architectures for message passing and shared memory
- Strategies for negotiation, consensus building, and task allocation
- Coordination algorithms including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Systems
- Applying reinforcement learning across multiple agents
- Exploring cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scaling Strategies
- Utilizing Ray for large-scale distributed multi-agent simulations
- Managing concurrency and synchronization challenges
- Optimizing parallel computation and shared resource handling
Human–Agent Collaboration
- Designing interfaces for effective human-in-the-loop coordination
- Implementing hybrid workflows with AI-assisted decision support
- Addressing ethical and operational considerations
Capstone Project
- Design and build a comprehensive multi-agent system in Python
- Demonstrate effective coordination and learning among agents
- Present simulation outcomes and key performance insights
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- Solid comprehension of reinforcement learning or AI agent design
- Familiarity with distributed systems and networking principles
Target Audience
- System architects developing collaborative or distributed AI systems
- Researchers focused on coordination mechanisms and collective intelligence
- Engineers creating hybrid human–agent or multi-agent workflows
