AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Availability: InStock
Author: Slevo

Create and train reinforcement learning plugins for autonomous agents.

Updated Apr 8, 2026

Search Tags:#plugin #q-learning #actor-critic #reinforcement-learning #offline-learning #agent-training #decision-transformer

Ready to run

npx skills add https://github.com/SlevoDev/s-tag --skill agentdb-learning-plugins-slevodev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/SlevoDev/s-tag/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/SlevoDev/s-tag --skill agentdb-learning-plugins-slevodev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables teams to create and train self-learning plugins for autonomous agents using AgentDB's RL algorithms, streamlining experimentation and deployment.

Core Features & Use Cases

Create plugins with templates for Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more.
Train and manage learning models using a unified adapter and API, store experiences, and evaluate performance.
Use cases include offline imitation learning, online policy learning, multi-task learning, and privacy-preserving distributed training.

Quick Start

Install and invoke the CLI to create a new learning plugin, then begin training with sample data.

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 a reinforcement learning plugin for an autonomous agent?▼

Train a reinforcement learning plugin by using a unified adapter to create templates, manage learning models, and persist experiences via CLI and API workflows. This framework supports offline, online, and mixed training scenarios for autonomous agents.

Which reinforcement learning algorithms are supported for agent training?▼

Supported reinforcement learning algorithms include Decision Transformer, Q-Learning, SARSA, and Actor-Critic. These templates allow teams to implement various policy learning and imitation learning strategies for autonomous agents.

Can I use offline learning and online policy learning in the same workflow?▼

Yes, you can use offline and online policy learning in the same workflow. The framework supports mixed training scenarios, allowing you to combine offline imitation learning with online policy updates and multi-task learning.

Do I need Node.js and AgentDB to create self-learning agent plugins?▼

Yes, creating self-learning agent plugins requires Node.js 18+ and AgentDB 1.0.7+. These dependencies provide the necessary environment and database adapter to persist training experiences and manage learning models.

What is the best way to manage and store experiences for decision-transformer models?▼

The best way to manage experiences for decision-transformer models is using the framework's unified adapter. It persists training data and evaluates model performance across CLI and API workflows for multi-task learning.