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KentwareDemo
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AgentDB Learning Plugins

Create and train reinforcement learning plugins using AgentDB's nine algorithms.

1|Updated Jun 3, 2026
Search Tags:#machine learning#agentdb#agent training#reinforcement learning#q-learning#decision transformer#actor-critic
One-click install
npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-learning-plugins-kentwaredemo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/KentwareDemo/RuView/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/KentwareDemo/RuView --skill agentdb-learning-plugins-kentwaredemo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Building self-improving agents requires implementing reinforcement learning from scratch, which is complex and error-prone. This Skill provides ready-to-use templates and APIs for training agents with nine RL algorithms through AgentDB. ## Core Features & Use Cases - Nine RL Algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning. - CLI Plugin Management: Create, list, and inspect learning plugins with npx agentdb commands and template scaffolding. - Training API: Store experiences as patterns, train models with configurable epochs and batch sizes, and retrieve similar experiences for inference. - Use Case: Train a game-playing agent by storing state-action-reward experiences in AgentDB, running 100 training epochs, then querying the learned model to suggest actions for new game states. ## Quick Start Ask the AI to create a Decision Transformer learning plugin with AgentDB and train it on stored agent experiences.

Frequently Asked Questions about AgentDB Learning Plugins

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a reinforcement learning plugin with AgentDB?▼

Run npx agentdb@latest create-plugin with a template flag such as -t decision-transformer and a name via -n my-agent. Use --dry-run to preview or list-templates to see all nine available algorithm templates.

Which AgentDB learning algorithm should I use for my agent?▼

Decision Transformer suits offline learning from logged experiences, Q-Learning fits discrete action spaces, SARSA is safer for risk-sensitive tasks, and Actor-Critic handles continuous actions. Choose based on your action space and whether online interaction is available.

How do I train an AgentDB learning model with the API?▼

Store experiences with adapter.insertPattern containing state, action, reward, and next_state, then call adapter.train with epochs and batchSize parameters. The method returns metrics including loss and training duration.

What are the requirements for using AgentDB learning plugins?▼

You need Node.js 18 or later and AgentDB v1.0.7 or higher accessed through agentic-flow. Basic familiarity with reinforcement learning concepts is recommended for configuring algorithms effectively.

Why is my AgentDB training not converging?▼

Training may fail to converge when the learning rate is too high. Lower it to 0.0001 in the train call, and for overfitting add a validationSplit of 0.2 or enable memory optimization during retrieval.

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