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FrekiManagarm
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AgentDB Learning Plugins

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

Updated May 8, 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/FrekiManagarm/d-chambaud --skill agentdb-learning-plugins-frekimanagarm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/FrekiManagarm/d-chambaud/tree/main/.agents/skills/agentdb-learning
Command: npx skills add https://github.com/FrekiManagarm/d-chambaud --skill agentdb-learning-plugins-frekimanagarm

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 that learn from experience using AgentDB's plugin system. ## Core Features & Use Cases - Nine RL Algorithms: Create plugins for Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning. - CLI and API Workflows: Scaffold plugins with npx agentdb create-plugin or train models programmatically via the agentic-flow adapter with experience storage and batch training. - Use Case: Imagine building a game-playing agent. Store state-action-reward experiences in AgentDB, train a Decision Transformer on logged episodes, then retrieve similar successful experiences at inference time to suggest actions with confidence scores. ## 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. The interactive wizard guides setup, and --dry-run previews the plugin without creating files.

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

Decision Transformer suits offline learning from logged experiences and imitation learning. Q-Learning fits discrete action spaces, SARSA fits safety-critical tasks, and Actor-Critic handles continuous action spaces like robotics control.

What are the requirements for using AgentDB learning plugins?▼

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

Why is my AgentDB training not converging?▼

Training often fails to converge when the learning rate is too high. Lower it to around 0.0001 in the train call, and use a validationSplit of 0.2 to detect overfitting during training.

Can I train AgentDB models incrementally as new data arrives?▼

Yes, incremental learning is supported by calling adapter.train with a small epoch count whenever enough new experiences accumulate. A common pattern triggers training every minute once more than 100 new experiences exist.

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