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.
Course Outline
Introduction to Multi-Agent Systems
- Overview of agents, environments, and interaction models
- Cooperation, competition, and autonomy in agentic systems
- Applications in logistics, robotics, and decision-making
Core Concepts of Agent Architecture
- Reactive vs. deliberative agents
- Communication protocols and coordination models
- Knowledge representation and shared state
Implementing Agents in Python
- Building agents using the Mesa framework
- Modeling environments and interactions
- Simulating agent behavior and visualization
Coordination and Communication
- Message passing and shared memory architectures
- Negotiation, consensus, and task allocation
- Coordination algorithms (contract net, market-based, swarm models)
Learning and Adaptation in Multi-Agent Systems
- Reinforcement learning for multiple agents
- Cooperative vs. competitive learning dynamics
- Using PettingZoo and Stable-Baselines3 for MARL
Distributed Computing and Scaling
- Using Ray for distributed multi-agent simulations
- Managing concurrency and synchronization
- Parallelizing computation and handling shared resources
Human–Agent Collaboration
- Designing interfaces for human-in-the-loop coordination
- Hybrid workflows with AI-assisted decision support
- Ethical and operational considerations
Capstone Project
- Design and implement a multi-agent system in Python
- Demonstrate coordination and learning among agents
- Present simulation results and performance insights
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Good understanding of reinforcement learning or AI agent design
- Familiarity with distributed systems and networking concepts
Audience
- System architects designing collaborative or distributed AI systems
- Researchers working on coordination and collective intelligence
- Engineers developing hybrid human–agent or multi-agent workflows
28 Hours
