How fast is the training performance?

By using WASM-accelerated neural inference, AgentDB models can train 10-100x faster than standard JavaScript-based machine learning implementations.

What is AgentDB Learning?

It is a suite of reinforcement learning plugins that allow Claude to create, train, and deploy agents that improve their behavior through experience using AgentDB's plugin system.

What are the prerequisites for using this skill?

You need Node.js 18+ and AgentDB v1.0.7 or higher. While templates are provided, a basic understanding of reinforcement learning concepts is recommended.

Which RL algorithms are supported?

The skill includes 9 algorithms: Decision Transformer (offline RL), Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

Can it learn from historical data without an environment?

Yes, the Decision Transformer template is specifically designed for offline reinforcement learning, allowing agents to learn from logged experiences and imitation learning.

AgentDB Reinforcement Learning

Name: AgentDB Reinforcement Learning
Author: while-basic

bywhile-basic

0•

Data Science & ML

Enables autonomous agents to learn and improve through experience using nine advanced reinforcement learning algorithms.

AgentDB Learning Plugins empower developers to integrate sophisticated reinforcement learning (RL) capabilities into their Claude Code workflows. By leveraging AgentDB's WASM-accelerated neural inference, this skill allows for the creation, training, and deployment of self-learning agents that optimize their behavior based on historical or real-time experience. Whether you are implementing offline RL with Decision Transformers for imitation learning or discrete decision-making via Q-Learning, this skill provides a robust framework for building agents that adapt and improve over time across diverse domains such as robotics, game logic, and complex resource allocation.

Key Features

01Advanced training techniques including prioritized experience replay and curriculum learning

02Access 9 distinct RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic

03WASM-accelerated neural inference providing 10-100x faster performance

04Support for both online interaction and offline RL from historical logs

05Integrated CLI for rapid plugin scaffolding, template management, and status tracking

060 GitHub stars

Use Cases

01Building autonomous agents that learn from historical user demonstrations or expert logs

02Optimizing complex decision-making processes in dynamic or uncertain environments

03Developing robust agents using adversarial training and multi-agent coordination patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add while-basic/c-flow agentdb-learning

For use in Claude.ai and ChatGPT

Download Skill