Multi-Agent Systems & Coordination in Python Training Course

Foundations of Multi-Agent Systems

• Overview of agents, environments, and interaction models
• Exploring cooperation, competition, and autonomy in agentic systems
• Real-world applications in logistics, robotics, and decision-making

Essential Agent Architecture Concepts

• Distinguishing between reactive and deliberative agents
• Communication protocols and coordination models
• Knowledge representation and managing shared state

Building Agents in Python

• Constructing agents with the Mesa framework
• Modelling environments and agent interactions
• Simulating agent behaviour and visualising results

Coordination and Communication Strategies

• Message passing and shared memory architectures
• 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 to multiple agents
• Cooperative versus competitive learning dynamics
• Leveraging PettingZoo and Stable-Baselines3 for multi-agent reinforcement learning

Distributed Computing and Scalability

• Utilising Ray for distributed multi-agent simulations
• Managing concurrency and synchronisation
• Parallelising computation and handling shared resources

Human–Agent Collaboration

• Designing interfaces for human-in-the-loop coordination
• Hybrid workflows featuring AI-assisted decision support
• Ethical and operational considerations

Capstone Project

• Design and implement a multi-agent system in Python
• Demonstrate coordination and learning processes among agents
• Present simulation outcomes and performance analysis

Summary and Future Directions