What are the prerequisites for using these plugins?

You will need Node.js 18 or higher, AgentDB v1.0.7 or higher (via agentic-flow), and a basic understanding of reinforcement learning concepts.

Is online interaction required for training models?

No, the Decision Transformer algorithm allows for offline reinforcement learning, enabling agents to learn directly from logged historical data or expert demonstrations without live environment interaction.

What algorithms are included in this skill?

It includes nine algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Curiosity-driven, Active Learning, Adversarial Training, Curriculum Learning, and Federated Learning.

How does WASM acceleration benefit the training process?

WebAssembly (WASM) acceleration allows for neural inference and model training to occur 10-100x faster than standard JavaScript implementations, making it suitable for performance-heavy applications.

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: Activer007

byActiver007

Data Science & ML

Builds and trains self-improving AI agents using nine reinforcement learning algorithms and WASM-accelerated inference.

About

AgentDB Learning Plugins empower developers to implement advanced reinforcement learning (RL) capabilities directly within their agentic workflows. By providing a suite of nine specialized algorithms—including Decision Transformers for offline learning and Actor-Critic for continuous control—this skill enables the creation of autonomous agents that learn from experience and optimize their behavior over time. Integrated with WASM-accelerated neural inference, it offers significant performance gains for real-time decision-making, while the streamlined CLI and API make it accessible for tasks ranging from robotics simulations to complex multi-agent coordination.

Key Features

Integrated CLI wizard for interactive plugin creation and template management
Advanced training techniques like Prioritized Experience Replay and Federated Learning
Access to nine RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training
Support for offline reinforcement learning to learn from historical datasets
0 GitHub stars

Use Cases

Implementing privacy-preserving collaborative learning across distributed systems
Developing simulations that require continuous control and policy optimization
Building autonomous agents that improve decision-making through trial and error

About

Key Features

Integrated CLI wizard for interactive plugin creation and template management
Advanced training techniques like Prioritized Experience Replay and Federated Learning
Access to nine RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training
Support for offline reinforcement learning to learn from historical datasets
0 GitHub stars

Use Cases

Implementing privacy-preserving collaborative learning across distributed systems
Developing simulations that require continuous control and policy optimization
Building autonomous agents that improve decision-making through trial and error