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AgentDB Learning Plugins

Create and train AI learning plugins with AgentDB's nine reinforcement algorithms.

1|2|Updated Nov 5, 2025
Search Tags:#TypeScript#plugins#AgentDB#npx#machine-learning#reinforcement-learning#offline-learning
One-click install
npx skills add https://github.com/LLM-Dev-Ops/observatory --skill agentdb-learning-plugins-llm-dev-ops
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/LLM-Dev-Ops/observatory/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/LLM-Dev-Ops/observatory --skill agentdb-learning-plugins-llm-dev-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a framework to create and train reinforcement learning plugins using AgentDB's nine reinforcement learning algorithms, enabling developers to deploy autonomous agents that learn from experience.

Core Features & Use Cases

  • Plugin-based RL library: Create, train, and deploy learning plugins for autonomous agents using algorithms such as decision-transformer, Q-learning, SARSA, and actor-critic.
  • CLI-first workflow: Scaffold plugins, browse templates, and manage plugins with simple npx commands.
  • Use Case Scenarios: Offline RL from logged data, online training with live environments, multi-agent experiments, and research prototyping.

Quick Start

Execute the following commands to scaffold and manage learning plugins: npx agentdb@latest create-plugin -t decision-transformer -n dt-agent npx agentdb@latest list-templates npx agentdb@latest plugin-info dt-agent

Frequently Asked Questions about AgentDB Learning Plugins

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

FAQPage Schema
How do I create and train reinforcement learning plugins for autonomous agents?▼

You create and train reinforcement learning plugins by using AgentDB's CLI to scaffold templates, then applying one of nine algorithms like Q-learning or actor-critic. This enables autonomous agents to learn from logged data or live environments.

What reinforcement learning algorithms are available for offline learning from logged data?▼

Available reinforcement learning algorithms include decision-transformer, Q-learning, SARSA, and actor-critic. These support offline RL from logged data, online training, value-based learning, policy gradients, and exploration tasks for research prototyping.

How do I scaffold a decision-transformer plugin using npx commands?▼

Scaffold a decision-transformer plugin by running npx agentdb@latest create-plugin -t decision-transformer -n dt-agent. You can browse other templates with npx agentdb@latest list-templates and inspect plugin details with plugin-info.

Do I need Node.js and AgentDB installed to build machine learning plugins?▼

Yes, building machine learning plugins requires Node.js 18+ and AgentDB v1.0.7+ installed via npm. These provide the runtime environment and CLI workflow needed to scaffold, train, and deploy reinforcement learning agents.

Can I use these reinforcement learning plugins for multi-agent experiments and research prototyping?▼

Yes, the plugin-based RL library supports multi-agent experiments, research prototyping, online training with live environments, and offline RL from logged data. You can deploy autonomous agents that learn from experience across these scenarios.

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