Multi-Agent Systems & Coordination in Python Training Course
This course delves into the design, coordination, and implementation of multi-agent systems (MAS) using Python. Participants will learn how to construct agents that communicate, collaborate, and adapt to achieve shared objectives within complex, dynamic environments.
This instructor-led live training (available online or onsite) is designed for advanced professionals aiming to develop and deploy multi-agent systems for intelligent automation, simulation, and decision-making applications.
Upon completion of this training, participants will be able to:
- Grasp the architecture and core principles of multi-agent systems.
- Develop agents capable of communication, coordination, and negotiation.
- Implement distributed environments for agent interactions.
- Apply reinforcement learning and planning techniques in multi-agent contexts.
- Simulate cooperative and competitive agent behaviours.
- Design hybrid workflows that integrate humans with intelligent agents.
Course Format
- Instructor-led lectures and live demonstrations.
- Hands-on exercises utilising open-source agent frameworks.
- Applied group project simulating a multi-agent scenario.
Course Customisation Options
- To request bespoke training for this course, please contact us to arrange.
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
- Modelling environments and interactions
- Simulating agent behaviour and visualisation
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
- Solid 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
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 5200 € + VAT*
Contact us for an exact quote and to hear our latest promotions
(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)
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