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Duration 28 hours
Course Outline
Foundations of Multi-Agent Systems
- Exploration of agents, their environments, and interaction paradigms
- The balance of cooperation, competition, and autonomy within agentic systems
- Practical applications in logistics, robotics, and complex decision-making
Essentials of Agent Architecture
- Distinguishing between reactive and deliberative agent models
- Design of communication protocols and coordination mechanisms
- Strategies for knowledge representation and maintaining shared state
Building Agents in Python
- Constructing robust agents using the Mesa framework
- Modeling dynamic environments and agent interactions
- Simulating agent behaviors and implementing visualization tools
Orchestration and Communication
- Architecting message passing and shared memory solutions
- Implementing negotiation, consensus building, and task allocation strategies
- Applying coordination algorithms such as contract net, market-based, and swarm models
Adaptive Learning in Multi-Agent Systems
- Applying reinforcement learning techniques to multi-agent settings
- Analyzing cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning (MARL)
Distributed Computing and Scalability
- Utilizing Ray to execute distributed multi-agent simulations
- Managing concurrency and synchronization challenges
- Optimizing parallel computation and handling shared resources efficiently
Human–Agent Collaboration
- Designing effective interfaces for human-in-the-loop coordination
- Developing hybrid workflows with AI-assisted decision support
- Navigating ethical and operational considerations in collaborative systems
Capstone Project
- Design and implementation of a comprehensive multi-agent system in Python
- Demonstrating effective coordination and adaptive learning among agents
- Presentation of simulation outcomes and key performance insights
Summary and Future Pathways
Requirements
- Advanced proficiency in Python programming
- Solid grasp of reinforcement learning or AI agent design patterns
- Working knowledge of distributed systems and networking fundamentals
Target Audience
- System architects focused on building collaborative or distributed AI ecosystems
- Researchers specializing in coordination mechanisms and collective intelligence
- Engineers engineering hybrid human–agent or multi-agent workflows
