Which reinforcement learning algorithms are included?

This skill provides 9 algorithms, including Decision Transformer (Offline RL), Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

What is the performance benefit of using this skill?

It utilizes WASM-accelerated neural inference, allowing you to train models 10-100x faster than standard pure-JavaScript implementations, making it suitable for real-time agent development.

What are the prerequisites for using AgentDB Learning Plugins?

You need Node.js 18 or higher and AgentDB v1.0.7+ (typically via agentic-flow) to properly initialize the adapters and utilize the WASM acceleration.

Can I use this for offline reinforcement learning?

Yes, the included Decision Transformer template is specifically optimized for offline RL, enabling agents to learn effectively from logged historical data and expert demonstrations.

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: Activer007

byActiver007

데이터 과학 및 ML

Integrates nine reinforcement learning algorithms to build, train, and deploy self-learning AI agents that evolve through experience.

소개

This skill empowers developers to implement advanced reinforcement learning (RL) capabilities directly into their agents using AgentDB's specialized plugin system. By providing access to nine distinct algorithms—including Decision Transformer for offline learning, Q-Learning for discrete actions, and Actor-Critic for continuous control—it bridges the gap between static code and adaptive AI behavior. With WASM-accelerated inference and built-in templates for various learning paradigms like Federated and Curriculum learning, it is ideal for creating agents that improve their performance over time using historical data or live environment interaction without requiring complex external infrastructure.

주요 기능

WASM-accelerated neural inference providing 10-100x faster training performance
Support for advanced paradigms like Active, Federated, and Multi-Task learning
Access to 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
0 GitHub stars
Integrated experience collection and memory optimization for better generalization
Interactive CLI wizard for rapid plugin creation from standardized templates

사용 사례

Developing privacy-preserving multi-agent systems using distributed federated learning
Implementing imitation learning from historical expert demonstrations via Decision Transformers
Training autonomous agents for complex decision-making and robotic navigation tasks

소개

주요 기능

WASM-accelerated neural inference providing 10-100x faster training performance
Support for advanced paradigms like Active, Federated, and Multi-Task learning
Access to 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
0 GitHub stars
Integrated experience collection and memory optimization for better generalization
Interactive CLI wizard for rapid plugin creation from standardized templates

사용 사례

Developing privacy-preserving multi-agent systems using distributed federated learning
Implementing imitation learning from historical expert demonstrations via Decision Transformers
Training autonomous agents for complex decision-making and robotic navigation tasks