Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 28 hours
Course Outline
Foundations of Multi-Agent Systems
- Exploring agents, their environments, and interaction paradigms
- Analyzing cooperation, competition, and autonomy in agentic architectures
- Real-world applications in logistics, robotics, and strategic decision-making
Essential Agent Architecture Principles
- Distinguishing between reactive and deliberative agent models
- Reviewing communication protocols and coordination frameworks
- Handling knowledge representation and shared state management
Building Agents with Python
- Constructing agent models using the Mesa framework
- Simulating environments and agent interactions
- Visualizing and analyzing agent behaviors
Synchronization and Communication Strategies
- Implementing message passing and shared memory structures
- Managing negotiation, consensus formation, and task distribution
- Applying coordination algorithms such as contract net, market-based, and swarm models
Adaptation and Learning in Multi-Agent Contexts
- Applying reinforcement learning to multiple agents
- Exploring cooperative versus competitive learning dynamics
- Leveraging PettingZoo and Stable-Baselines3 for Multi-Agent Reinforcement Learning
Distributed Computing and Scalability
- Utilizing Ray for large-scale distributed multi-agent simulations
- Controlling concurrency and synchronization processes
- Optimizing parallel computation and shared resource management
Human–Agent Synergy
- Creating interfaces for human-in-the-loop coordination
- Integrating AI-assisted decision support into hybrid workflows
- Addressing ethical and operational considerations
Capstone Project
- Architecting and building a complete multi-agent system in Python
- Demonstrating effective coordination and learning capabilities among agents
- Presenting simulation outcomes and key performance analyses
Conclusion and Future Directions
Requirements
- Advanced command of Python programming
- Solid grasp of reinforcement learning or AI agent design principles
- Working knowledge of distributed systems and networking fundamentals
Target Audience
- System architects building collaborative or distributed AI infrastructures
- Researchers specializing in coordination mechanisms and collective intelligence
- Engineers implementing hybrid human–agent or complex multi-agent workflows
