Is multi-agent training supported?

Yes, the skill provides patterns for collecting experiences from multiple agents and using Federated Learning to train shared models while preserving data privacy.

How does WASM acceleration help?

WASM-accelerated neural inference allows the learning plugins to process data and train models 10-100x faster than standard JavaScript implementations.

Which RL algorithms are included in this skill?

It includes 9 core algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

What are the technical prerequisites?

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

Can I train models without live environment interaction?

Yes, using the Decision Transformer template, you can perform offline reinforcement learning by training on historical logs and expert demonstrations.

AgentDB Reinforcement Learning

Name: AgentDB Reinforcement Learning
Author: nbossn

bynbossn

0•

Data Science & ML

Integrates nine advanced reinforcement learning algorithms into Claude to build self-optimizing autonomous agents that learn from experience.

The AgentDB Learning Plugins skill empowers developers to implement complex reinforcement learning (RL) behaviors within their Claude-driven projects. By providing access to nine distinct algorithms—including Decision Transformers for offline learning and Actor-Critic for continuous control—it allows Claude to create, train, and deploy agents that improve their decision-making over time. The skill leverages WASM-accelerated neural inference for high-performance training and includes comprehensive tools for experience collection, replay buffers, and multi-agent coordination, making it ideal for developers building sophisticated autonomous systems, game AI, or robotics simulations.

Key Features

01Advanced training techniques like Prioritized Experience Replay and Multi-Task Learning

02Support for offline reinforcement learning from historical data logs

03Access to 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic

04Interactive CLI wizard for rapid plugin scaffolding and template management

050 GitHub stars

06WASM-accelerated neural inference for 10-100x faster training performance

Use Cases

01Building label-efficient AI systems with Human-in-the-loop Active Learning

02Creating autonomous agents that refine their behavior through trial-and-error experience

03Implementing robust decision-making in complex environments using Adversarial and Curriculum Learning

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nbossn/claude-code-base agentdb-learning

For use in Claude.ai and ChatGPT

Key Features

01Advanced training techniques like Prioritized Experience Replay and Multi-Task Learning

02Support for offline reinforcement learning from historical data logs

03Access to 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic

04Interactive CLI wizard for rapid plugin scaffolding and template management

050 GitHub stars

06WASM-accelerated neural inference for 10-100x faster training performance

Use Cases

01Building label-efficient AI systems with Human-in-the-loop Active Learning

02Creating autonomous agents that refine their behavior through trial-and-error experience

03Implementing robust decision-making in complex environments using Adversarial and Curriculum Learning