Course Outline

Introduction to Autonomous Agents

  • What are autonomous agents?
  • Key characteristics and functionalities
  • Applications across industries

Core Concepts of Agent Design

  • Agent architectures and types
  • Understanding agent environments
  • Multi-agent systems and interactions

Building AI Agents with Reinforcement Learning

  • Overview of reinforcement learning (RL)
  • Designing reward systems for agents
  • Training agents using OpenAI Gym

Developing Practical Applications

  • Creating recommendation systems with autonomous agents
  • Implementing agents for process automation
  • Using agents for environmental monitoring and sensing

Integrating Agents into Existing Systems

  • Communicating with external APIs
  • Embedding agents in cloud-based architectures
  • Ensuring compatibility with existing tools

Addressing Challenges and Ethical Considerations

  • Dealing with unexpected agent behavior
  • Ensuring fairness and inclusivity
  • Compliance with legal and ethical standards

Exploring Advanced Agent Capabilities

  • Incorporating natural language processing
  • Leveraging multi-agent collaboration
  • Enhancing decision-making with AI

Future Trends in Autonomous Agents

  • Emerging technologies in agent design
  • Expanding applications in diverse industries
  • Opportunities and challenges in autonomous systems

Summary and Next Steps

Requirements

  • Basic understanding of machine learning concepts
  • Familiarity with Python programming
  • Experience with algorithm design and implementation

Audience

  • AI developers
  • Data scientists
  • Software engineers
 21 Hours

Delivery Options

Private Group Training

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  • Training scheduled on a date of your choice
  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.

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