Is this skill suitable for offline reinforcement learning?

Yes, it specifically includes the Decision Transformer template, which is highly recommended for offline RL and learning from logged experiences without live interaction.

Which reinforcement learning algorithms are included?

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

What are the technical requirements for using AgentDB Learning Plugins?

You will need Node.js 18 or higher and AgentDB v1.0.7+ (typically installed via agentic-flow) to run the training and inference engines.

How does WASM acceleration benefit the training process?

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

AgentDB Learning Plugins

Name: AgentDB Learning Plugins
Author: Microck

byMicrock

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데이터 과학 및 ML

Empowers Claude to create, train, and deploy self-learning autonomous agents using nine specialized reinforcement learning algorithms.

소개

AgentDB Learning Plugins provides a high-performance framework for implementing reinforcement learning (RL) directly within Claude's development environment. By leveraging AgentDB's WASM-accelerated neural inference, developers can build agents that evolve through experience using diverse paradigms—from offline learning with Decision Transformers to real-time policy optimization with Actor-Critic models. Whether you are building game-playing AI, optimizing resource allocation, or creating robust autonomous systems, this skill provides the templates, CLI tools, and API integrations necessary to integrate sophisticated machine learning workflows into your agentic applications.

주요 기능

Interactive CLI wizard for rapid plugin creation and template management.
Seamless integration with agentic-flow for reasoning-based decision making.
89 GitHub stars
Advanced training capabilities like prioritized experience replay and multi-task learning.
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic.
WASM-accelerated neural inference delivering 10-100x faster training performance.

사용 사례

Creating robust AI systems for safety-critical tasks using conservative SARSA or adversarial training.
Building autonomous agents that optimize their behavior through historical interaction data.
Implementing imitation learning to train agents based on expert human demonstrations.

소개

주요 기능

Interactive CLI wizard for rapid plugin creation and template management.
Seamless integration with agentic-flow for reasoning-based decision making.
89 GitHub stars
Advanced training capabilities like prioritized experience replay and multi-task learning.
Support for 9 RL algorithms including Decision Transformer, Q-Learning, and Actor-Critic.
WASM-accelerated neural inference delivering 10-100x faster training performance.

사용 사례

Creating robust AI systems for safety-critical tasks using conservative SARSA or adversarial training.
Building autonomous agents that optimize their behavior through historical interaction data.
Implementing imitation learning to train agents based on expert human demonstrations.