Verified:GitHub

AgentDB Learning Plugins

Create and train AI learning plugins with 9 reinforcement learning algorithms.

Updated Mar 5, 2026

Search Tags:#machine learning #ai agents #agentdb #reinforcement learning #q-learning #decision transformer #actor-critic

Ready to run

npx skills add https://github.com/bjorkgard/convention-hosts --skill agentdb-learning-plugins-bjorkgard

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/bjorkgard/convention-hosts/tree/main/.agents/skills/agentdb-learning
Command: npx skills add https://github.com/bjorkgard/convention-hosts --skill agentdb-learning-plugins-bjorkgard

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 variety of reinforcement learning algorithms, allowing agents to improve their behavior through experience.

Core Features & Use Cases

Reinforcement Learning Algorithms: Access to 9 RL algorithms including Decision Transformer, Q-Learning, SARSA, and Actor-Critic.
Agent Training: Train models to learn from collected experiences, optimizing decision-making.
Performance: Utilizes WASM-accelerated neural inference for faster training.
Use Case: Develop a self-learning agent for a game that improves its strategy by playing against itself and learning from the outcomes of each game.

Quick Start

Use the agentdb CLI to create a new learning plugin for the decision-transformer algorithm named 'my-rl-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 algorithms?▼

You train AI agents using reinforcement learning algorithms by creating a learning plugin that supports methods like Q-Learning, Actor-Critic, and Decision Transformer to optimize decision-making through collected experiences.

What reinforcement learning algorithms are available for agent training?▼

Available reinforcement learning algorithms for agent training include 9 distinct options such as Decision Transformer, Q-Learning, SARSA, and Actor-Critic, covering offline RL, value-based methods, and policy gradients.

How do I create a reinforcement learning plugin with the agentdb CLI?▼

To create a reinforcement learning plugin with the agentdb CLI, use the command to generate a new plugin for your chosen algorithm, such as naming a decision-transformer agent to begin the training setup.

Does AgentDB support offline reinforcement learning and policy gradients?▼

Yes, AgentDB supports offline reinforcement learning and policy gradients, providing a framework that allows AI agents to learn from collected experiences and optimize behavior using these specific methods.

How is neural inference accelerated during agent training?▼

Neural inference is accelerated during agent training by leveraging WebAssembly (WASM), which provides faster processing speeds for the reinforcement learning models integrated with the AgentDB API.

Can I use this to develop a self-learning agent for a game environment?▼

Yes, you can develop a self-learning agent for a game environment that improves its strategy by playing against itself, learning from the outcomes of each game to optimize future decisions.