What are the requirements for training models?

You need Node.js 18+ and AgentDB v1.0.7+. The system uses WASM-accelerated neural inference, allowing for efficient training on standard development machines.

Do I need extensive ML knowledge to use this skill?

While a basic understanding of RL concepts is recommended, the skill provides CLI wizards and templates to help developers get started quickly with pre-configured models and configurations.

Can I train agents using historical data without an active environment?

Yes, the Decision Transformer algorithm is specifically designed for offline reinforcement learning, enabling agents to learn from expert demonstrations or logged historical experiences.

Which reinforcement learning algorithms are included?

The skill includes 9 algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Curiosity-driven, Active Learning, Adversarial Training, Curriculum Learning, and Multi-Task Learning.

AgentDB Reinforcement Learning Plugins

Name: AgentDB Reinforcement Learning Plugins
Author: Activer007

byActiver007

Data Science & ML

Deploys and trains sophisticated reinforcement learning algorithms to create autonomous agents that improve through experience and reasoning.

About

This skill provides a comprehensive suite of nine reinforcement learning (RL) algorithms—including Decision Transformer, Q-Learning, and Actor-Critic—integrated directly into the AgentDB ecosystem. It enables developers to build self-learning agents capable of optimizing their behavior based on historical logs or real-time interaction, leveraging WASM-accelerated neural inference for high-speed performance. By combining traditional RL with vector-based reasoning, it allows for the creation of robust, adaptive AI systems that handle complex decision-making tasks across diverse domains like gaming, robotics, and workflow automation.

Key Features

Access to 9 advanced RL algorithms including Decision Transformer, SARSA, and Actor-Critic
0 GitHub stars
Advanced training workflows including prioritized experience replay and multi-agent training
Seamless integration with AgentDB for persistent experience storage and retrieval
High-performance neural inference powered by WASM-accelerated computation
Support for both offline RL (learning from logs) and online exploration-based learning

Use Cases

Implementing human-in-the-loop systems that refine behavior based on active learning feedback
Optimizing complex decision-making workflows in business processes through historical data analysis
Building autonomous game-playing agents or navigation bots that learn from environments

About

Key Features

Access to 9 advanced RL algorithms including Decision Transformer, SARSA, and Actor-Critic
0 GitHub stars
Advanced training workflows including prioritized experience replay and multi-agent training
Seamless integration with AgentDB for persistent experience storage and retrieval
High-performance neural inference powered by WASM-accelerated computation
Support for both offline RL (learning from logs) and online exploration-based learning

Use Cases

Implementing human-in-the-loop systems that refine behavior based on active learning feedback
Optimizing complex decision-making workflows in business processes through historical data analysis
Building autonomous game-playing agents or navigation bots that learn from environments