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AgentDB Learning Plugins

Train self-learning AI agents with 9 reinforcement learning algorithms via AgentDB plugins.

5|Updated Oct 23, 2025
Search Tags:#machine learning#ai agents#agentdb#training#reinforcement learning#q-learning#decision transformer
One-click install
npx skills add https://github.com/wrsmith108/vibe-to-docker --skill agentdb-learning-plugins-wrsmith108
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/wrsmith108/vibe-to-docker/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/wrsmith108/vibe-to-docker --skill agentdb-learning-plugins-wrsmith108

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the creation and training of AI learning plugins using AgentDB's reinforcement learning algorithms, allowing agents to improve their behavior through experience.

Core Features & Use Cases

  • Reinforcement Learning: Access 9 distinct RL algorithms including Decision Transformer, Q-Learning, SARSA, and Actor-Critic.
  • Plugin System: Create, train, and deploy self-learning plugins for autonomous agents.
  • Performance: Leverage WASM-accelerated neural inference for 10-100x faster model training.
  • Use Case: Build a game-playing agent that learns optimal strategies by playing against itself, using the Decision Transformer algorithm to learn from past game states and outcomes.

Quick Start

Use the agentdb CLI to create a new learning plugin for a Q-learning agent named 'my-q-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 train AI agents using reinforcement learning with AgentDB?▼

The Skill supports offline RL, value-based learning, and policy gradients. It includes algorithms like SARSA and Actor-Critic, enabling agents to learn optimal strategies autonomously from past states and outcomes.

How do I create a Q-learning plugin for an autonomous agent?▼

You can train a self-learning plugin by running the agentdb CLI to create a Q-learning agent. The plugin then leverages WASM-accelerated neural inference to process game states and update optimal strategies.

What reinforcement learning algorithms are available for training self-learning agents?▼

The Skill provides 9 reinforcement learning algorithms, including Decision Transformer, Q-Learning, SARSA, and Actor-Critic. These support offline RL, value-based learning, and policy gradients for autonomous agents.

Does AgentDB support offline reinforcement learning and policy gradients?▼

Yes, AgentDB supports offline RL, value-based learning, and policy gradients. This allows you to train agents using past data without live environment interaction, accelerated by WASM neural inference.

Do I need a specific AgentDB version to use WASM-accelerated neural inference?▼

WASM-accelerated neural inference requires AgentDB v1.0.7 or higher. This version provides 10-100x faster model training and integrates with the plugin system for pattern insertion and reinforcement learning workflows.

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