What reinforcement learning algorithms are included?

The skill includes 9 algorithms: Decision Transformer (offline RL), Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning.

Is AgentDB Learning compatible with multi-agent systems?

Yes, it specifically supports Multi-Agent Training and Federated Learning, allowing multiple agents to contribute to and benefit from shared learning models while preserving data privacy.

How does WASM acceleration improve performance?

WASM-accelerated neural inference allows the models to perform training and inference 10-100x faster than standard JavaScript implementations, enabling real-time learning in Node.js environments.

What are the technical requirements for this skill?

You need Node.js 18 or higher and AgentDB v1.0.7 or later (installed via agentic-flow) to use the learning plugins and API.

Can I train agents without direct environment interaction?

Yes, by using the Decision Transformer template, you can implement Offline Reinforcement Learning to train agents using only historical data or expert demonstrations.

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: ricable

byricable

•

Data Science & ML

Integrate 9 reinforcement learning algorithms to build self-improving AI agents that learn from experience and optimize behavior autonomously.

About

AgentDB Learning Plugins provide a comprehensive toolkit for implementing 9 distinct reinforcement learning algorithms within AI agent workflows. From offline RL like Decision Transformers to value-based methods like Q-Learning and SARSA, this skill enables agents to collect experiences, train on historical data, and refine their decision-making processes. Leveraging WASM-accelerated neural inference for high performance, it allows developers to implement sophisticated learning patterns such as curriculum learning, multi-agent coordination, and active learning with minimal boilerplate, making it ideal for creating agents that grow more efficient over time.

Key Features

Interactive CLI wizard for rapid plugin scaffolding and template management
Versatile learning modes including Offline, Federated, Multi-Task, and Curriculum learning
2 GitHub stars
Support for 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training and execution
Unified API for experience collection, prioritized replay buffers, and incremental learning

Use Cases

Implementing robust multi-agent systems that share knowledge via federated learning protocols
Building autonomous agents that improve task success rates over time through real-world experience
Training agents on historical logs using Decision Transformers for safe, offline behavior cloning

About

Key Features

Interactive CLI wizard for rapid plugin scaffolding and template management
Versatile learning modes including Offline, Federated, Multi-Task, and Curriculum learning
2 GitHub stars
Support for 9 reinforcement learning algorithms including Decision Transformer and Actor-Critic
WASM-accelerated neural inference for 10-100x faster model training and execution
Unified API for experience collection, prioritized replay buffers, and incremental learning

Use Cases

Implementing robust multi-agent systems that share knowledge via federated learning protocols
Building autonomous agents that improve task success rates over time through real-world experience
Training agents on historical logs using Decision Transformers for safe, offline behavior cloning