LogoLogo
  • Welcome to Composabl
  • Get Started
  • Reference
    • CLI Reference
    • SDK Reference
    • Glossary
    • Sample Use Cases
  • Tutorials
    • Industrial Mixer
      • Get Started
      • Deep Reinforcement Learning
      • Strategy Pattern
      • Strategy Pattern with a Perception Layer
      • Plan-Execute Pattern
  • Establish a Simulation Environment
    • Simulation Overview
    • Connect a Simulator to Composabl
    • Composabl Simulation API
  • Build Multi-Agent Systems
    • Anatomy of a Multi-Agent System
    • Create a Use Case
    • Set Goals, Constraints, and Success Criteria
    • Create Skill Agents
      • Create Skill Agents
      • Create Skill Agents with Rewards Using the SDK
      • Configure Programmed Algorithms as Skill Agents
      • Configure API Connections to Third-Party Software as Skill Agents
    • Orchestrate Skill Agents
    • Configure Scenarios
    • Add a Perception Layer
      • Create a New Perceptor
      • Configure an ML Model as a Perceptor
      • Configure an LLM Model as a Perceptor
    • Publish Skill Agent Components to the UI
  • Train Agents
    • Submit a Training Job through the UI
    • Analyze Agent System Behavior
      • View Training Session Information
      • Analyze Data in Detail with the Historian
  • Evaluate Performance
    • Set KPI and ROI
    • Analyze Data
  • Deploy Agents
    • Access a Trained Agent System
    • Deploy an Agent System in a Container
    • Deploy an Agent System as an API
    • Connect Runtime Container to Your Operation
    • Connecting to Agent System Runtime and Plotting Results of Agent System Operations
  • clusters
    • Creating a Cluster
      • Manual
      • Automated
      • Azure
    • Connecting a Cluster
  • Troubleshooting
    • Resolving Certificate Issues for Installing the Composabl SDK on WSL
Powered by GitBook
On this page
Export as PDF
  1. Build Multi-Agent Systems

Create Skill Agents

PreviousSet Goals, Constraints, and Success CriteriaNextCreate Skill Agents

Last updated 27 days ago

Skills are the foundational building blocks for your intelligent agent system. They take action to achieve goals in key scenarios where your agent system needs to succeed. To build an agent with Machine Teaching, you will create multiple skill agents and then orchestrate them together.

You can use three different types of skill agents within Composabl.

  • Create a to implement a that uses DRL to make decisions. You can set up your teacher to or to . The skill agent will then train and practice in simulation until it can make decisions successfully.

  • Create a to implement a that makes decisions based on programming rather than learning. A controller can use math, rules, optimization, or any other technology that can be expressed in a Python program. and publish them to the UI to use in agent systems.

  • Create a to . Selectors are specialized skills that direct the agent system how to choose between different decision-making skills. Selectors can be either learned or programmed.

You can also create skills in two different ways, using the UI and using the SDK. If you use the SDK, you can then to be included in agent system designs.

For learned skills you can use either the UI or the SDK successfully to create teachers, but the SDK includes some fine-tuning options that are not available in the UI.

Controllers for programmed skills can be created only through the SDK. They can then be published to the UI for use in agent systems.

You can use either the UI or the SDK to create selectors.

train the skill agent using goals
train with rewards
Configure controllers with the SDK
orchestrate skills together
publish the skills to the UI