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

データサイエンスとML

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

概要

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.

主な機能

Advanced training techniques including prioritized experience replay and curriculum learning
Access 9 distinct RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
WASM-accelerated neural inference providing 10-100x faster performance
Support for both online interaction and offline RL from historical logs
Integrated CLI for rapid plugin scaffolding, template management, and status tracking
0 GitHub stars

ユースケース

Building autonomous agents that learn from historical user demonstrations or expert logs
Optimizing complex decision-making processes in dynamic or uncertain environments
Developing robust agents using adversarial training and multi-agent coordination patterns

概要

主な機能

Advanced training techniques including prioritized experience replay and curriculum learning
Access 9 distinct RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
WASM-accelerated neural inference providing 10-100x faster performance
Support for both online interaction and offline RL from historical logs
Integrated CLI for rapid plugin scaffolding, template management, and status tracking
0 GitHub stars

ユースケース

Building autonomous agents that learn from historical user demonstrations or expert logs
Optimizing complex decision-making processes in dynamic or uncertain environments
Developing robust agents using adversarial training and multi-agent coordination patterns