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AgentDB Learning Plugins

Train AI agents with nine reinforcement learning algorithms and WASM-accelerated inference.

2|Updated Jan 25, 2026
Search Tags:#agentdb#reinforcement learning#q-learning#decision transformer#actor-critic#ai training
One-click install
npx skills add https://github.com/EarthmanWeb/claude-flow-plugin --skill agentdb-learning-plugins-earthmanweb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/EarthmanWeb/claude-flow-plugin/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/EarthmanWeb/claude-flow-plugin --skill agentdb-learning-plugins-earthmanweb

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 a suite of nine reinforcement learning algorithms, allowing agents to improve their behavior through experience.

Core Features & Use Cases

  • Reinforcement Learning Algorithms: Access to Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more.
  • Performance: WASM-accelerated neural inference for faster model training.
  • Use Case: When building self-learning agents that need to optimize their decision-making processes based on trial and error, such as in game-playing or robotic control scenarios.

Quick Start

Use the agentdb create-plugin command with the decision-transformer template to build a new 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 train AI agents with reinforcement learning using Q-learning or Actor-Critic?▼

Train AI agents with reinforcement learning by applying algorithms like Q-learning or Actor-Critic to enable self-learning through trial and error. This Skill provides nine RL algorithms for behavioral optimization in scenarios like game-playing or robotic control.

What reinforcement learning algorithms are available for offline training?▼

Available reinforcement learning algorithms include Decision Transformer, Q-Learning, SARSA, and Actor-Critic. These support offline RL, value-based learning, and policy gradients to improve agent decision-making based on experience.

How do I create a reinforcement learning plugin for a Decision Transformer?▼

Create a reinforcement learning plugin by using the agentdb create-plugin command with the decision-transformer template. This initializes a learning plugin to train your AI agent.

Can I use WASM-accelerated inference for faster reinforcement learning model training?▼

Yes, you can use WASM-accelerated neural inference for faster reinforcement learning model training. This accelerates the processing speed required for agents to improve their behavior through experience.

Does AgentDB support managing and deploying reinforcement learning plugins?▼

Yes, AgentDB supports managing and deploying reinforcement learning plugins. It integrates directly with the learning algorithms to handle the lifecycle of self-learning agents optimizing their decision-making processes.

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