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Course Outline
Foundations of Multi-Agent Systems
- Overview of agents, their environments, and interaction models
- Examining cooperation, competition, and autonomy within agentic systems
- Real-world applications in logistics, robotics, and decision-making
Foundational Agent Architecture
- Distinguishing between reactive and deliberative agents
- Exploring communication protocols and coordination models
- Methods for knowledge representation and shared state management
Agent Implementation in Python
- Constructing agents using the Mesa framework
- Modeling environments and defining interaction dynamics
- Simulating agent behavior and generating visualizations
Coordination and Communication Mechanisms
- Architectures for message passing and shared memory
- Strategies for negotiation, reaching consensus, and task allocation
- Coordination algorithms, including contract net, market-based, and swarm models
Learning and Adaptation in Multi-Agent Contexts
- Applying reinforcement learning to multi-agent scenarios
- 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
- Optimizing computation through parallelization and shared resource handling
Collaboration Between Humans and Agents
- Designing interfaces for human-in-the-loop coordination
- Creating hybrid workflows supported by AI-driven decision assistance
- Addressing ethical and operational considerations
Capstone Project
- Designing and implementing a comprehensive multi-agent system in Python
- Demonstrating agent coordination and learning capabilities
- Presenting simulation outcomes and key performance insights
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A solid grasp of reinforcement learning or AI agent design
- Knowledge of distributed systems and networking principles
Target Audience
- System architects involved in building collaborative or distributed AI systems
- Researchers focusing on coordination mechanisms and collective intelligence
- Engineers creating hybrid workflows that combine human and multi-agent interactions
28 Hours
