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AgentDB Learning Plugins

Build and train AI learning plugins with AgentDB RL templates.

Updated Aug 23, 2026
Search Tags:#typescript#cli#agentdb#plugins#training#reinforcement-learning#offline-rl
One-click install
npx skills add https://github.com/cedriking/zenda --skill agentdb-learning-plugins-cedriking
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/cedriking/zenda/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/cedriking/zenda --skill agentdb-learning-plugins-cedriking

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines building and training AI learning plugins by leveraging AgentDB's reinforcement learning algorithms, reducing setup time and enabling rapid experimentations with agents that learn from experience.

Core Features & Use Cases

  • 9 RL algorithms templates including decision-transformer, q-learning, sarsa, actor-critic, and more for quick plugin bootstrap.
  • CLI and API tooling for plugin creation, configuration, training, and deployment to autonomous agents.
  • Integrated experience storage and reasoning workflows to improve learning efficiency and policy quality.
  • Use Case: develop self-improving agents for game-playing or robotics simulations that improve through logged experiences.

Quick Start

Launch the plugin wizard with npx agentdb@latest create-plugin to begin building a learning plugin.

Frequently Asked Questions about AgentDB Learning Plugins

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build reinforcement learning plugins for autonomous agents?▼

You can build reinforcement learning plugins using AgentDB's RL suite, which provides CLI and API tooling for plugin creation, configuration, and training across autonomous agents. Run npx agentdb@latest create-plugin to bootstrap a template.

What offline and online RL algorithms are available for TypeScript agents?▼

The plugin templates include 9 reinforcement learning algorithms such as decision-transformer, q-learning, sarsa, and actor-critic. These support both offline and online RL workflows for collecting experiences, training, and evaluating autonomous agents.

Do I need Node.js 18 and AgentDB to train self-improving AI agents?▼

Yes, training self-improving agents requires Node.js 18+ and AgentDB v1.0.7+ via the agentic-flow package. These dependencies provide the integrated experience storage and reasoning workflows necessary for learning efficiency.

Can I use decision-transformer and q-learning templates for game-playing simulations?▼

Yes, the RL plugin templates are designed for developing self-improving agents in game-playing or robotics simulations. Agents learn and improve policies through logged experiences stored and reasoned over within the AgentDB ecosystem.

What's the best way to bootstrap an AI training plugin with AgentDB?▼

The quickest way to bootstrap an AI training plugin is launching the plugin wizard with npx agentdb@latest create-plugin. This CLI tool generates a template from 9 available RL algorithms, streamlining setup for rapid experimentation.

How does experience storage work when training offline RL plugins?▼

Offline RL plugins use integrated experience storage to collect and log agent interactions. This stored data feeds directly into the training workflows, improving learning efficiency and policy quality without requiring live environment interaction.

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