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AgentDB Learning Plugins

Create and train reinforcement learning plugins for autonomous agents via CLI/API.

6|3|Updated Dec 3, 2025
Search Tags:#agentdb#plugins#wasm#q-learning#actor-critic#reinforcement-learning#offline-rl
One-click install
npx skills add https://github.com/pacphi/ampel --skill agentdb-learning-plugins-pacphi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/pacphi/ampel/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/pacphi/ampel --skill agentdb-learning-plugins-pacphi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides access to 9 reinforcement learning algorithms via AgentDB's plugin system. Create, train, and deploy learning plugins for autonomous agents that improve through experience. Includes offline RL (Decision Transformer), value-based learning (Q-Learning, SARSA), policy gradients (Actor-Critic), and advanced techniques. Performance gains come from WASM-accelerated neural inference.

Core Features & Use Cases

  • Offline RL / Decision Transformer: Learn from logged experiences and demonstrations without online interaction.
  • Value-based Learning: Q-Learning and SARSA for discrete action spaces with strong sample efficiency.
  • Policy Gradients: Actor-Critic and related methods for continuous or complex actions.
  • Learning Plugins Lifecycle: Create, train, and deploy learning plugins that improve through experience.
  • Performance: WASM-accelerated inference speeds up training.

Quick Start

Use the AgentDB CLI to create a learning plugin and integrate it with your agent framework, then store training experiences and trigger training as needed.

Frequently Asked Questions about AgentDB Learning Plugins

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

FAQPage Schema
How do I train reinforcement learning agents with offline data?▼

Offline RL with Decision Transformer trains agents from logged experiences and demonstrations without live interaction. AgentDB's plugin system provides this via WASM-accelerated inference, enabling learning from historical data across simulation, gaming, and robotics tasks.

Can I use Q-Learning and Actor-Critic algorithms with AgentDB?▼

Yes. AgentDB Learning Plugins include Q-Learning and SARSA for discrete action spaces, plus Actor-Critic for continuous or complex actions. All 9 algorithms run with WASM acceleration and integrate via the AgentDB CLI and API on Node.js 18+.

What's the fastest way to create and deploy a learning plugin?▼

Use the AgentDB CLI to create a learning plugin, store training experiences, and trigger training directly. WASM-accelerated neural inference speeds execution. The plugin integrates with your agent framework and deploys via CLI or API without external dependencies.

Do I need prior reinforcement learning experience to build plugins?▼

AgentDB abstracts RL implementation details through its plugin lifecycle. You define agents and store experiences; the system handles algorithm selection and training. Knowledge of your task domain matters more than RL theory for initial use.

What agent optimization scenarios does offline RL solve?▼

Offline RL optimizes agent behavior in resource-management, robotics, gaming, and simulation without online trial-and-error. Decision Transformer learns directly from logged demonstrations, reducing real-world interaction costs and improving sample efficiency.

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