Multi-Agent Systems & Coordination in Python Training Course

Introduction to Multi-Agent Systems

• Overview of agents, environments, and interaction models.
• Cooperation, competition, and autonomy within 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 management.

Implementing Agents in Python

• Building agents using the Mesa framework.
• Modeling environments and agent interactions.
• Simulating agent behavior and generating visualizations.

Coordination and Communication

• Message passing and shared memory architectures.
• Negotiation, consensus formation, and task allocation.
• Coordination algorithms, including contract net, market-based, and swarm models.

Learning and Adaptation in Multi-Agent Systems

• Reinforcement learning for multiple agents.
• Cooperative vs. competitive learning dynamics.
• Utilizing PettingZoo and Stable-Baselines3 for MARL.

Distributed Computing and Scaling

• Leveraging 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 incorporating 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