How does WASM acceleration benefit the learning process?

WASM-accelerated neural inference allows models to train and perform inference 10 to 100 times faster than standard JavaScript implementations, making it suitable for complex agent tasks.

Can I use this for offline learning?

Yes, the Decision Transformer template is specifically optimized for offline reinforcement learning, allowing agents to learn from logged historical data without requiring live environment interaction.

Is reinforcement learning expertise required to use this skill?

While a basic understanding of RL is recommended, the skill provides interactive CLI templates and pre-configured wizards that simplify the creation and deployment of learning plugins.

What are the system requirements?

You need Node.js 18 or higher and AgentDB v1.0.7+ (typically installed via the agentic-flow package).

Which reinforcement learning algorithms are supported?

The skill supports nine algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: ellisapotheosis

byellisapotheosis

数据科学与机器学习

Equips Claude with nine reinforcement learning algorithms to build self-learning agents that optimize behavior through experience and training.

关于

The AgentDB Learning Plugins skill provides a robust framework for implementing reinforcement learning (RL) within autonomous agent workflows. By offering access to nine distinct algorithms—including Decision Transformers, Q-Learning, and Actor-Critic—it allows developers to create agents that learn from historical data or live interactions. Featuring WASM-accelerated neural inference for significant performance gains, this skill is essential for building sophisticated AI systems that require adaptive decision-making, imitation learning, or distributed training across multi-agent environments.

主要功能

Interactive CLI wizard for rapid plugin scaffolding and template management
0 GitHub stars
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and SARSA
Comprehensive API for experience collection, batch training, and performance evaluation
WASM-accelerated neural inference for 10-100x faster training performance
Advanced learning modes including Federated, Multi-Task, and Curriculum Learning

使用场景

Implementing imitation learning to train agents based on expert human demonstrations
Developing robust multi-agent systems with privacy-preserving Federated Learning
Building autonomous agents that improve their task success rate through experience replay

关于

主要功能

Interactive CLI wizard for rapid plugin scaffolding and template management
0 GitHub stars
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and SARSA
Comprehensive API for experience collection, batch training, and performance evaluation
WASM-accelerated neural inference for 10-100x faster training performance
Advanced learning modes including Federated, Multi-Task, and Curriculum Learning

使用场景

Implementing imitation learning to train agents based on expert human demonstrations
Developing robust multi-agent systems with privacy-preserving Federated Learning
Building autonomous agents that improve their task success rate through experience replay