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FrekiManagarm
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AgentDB Learning Plugins

Build reinforcement learning plugins for AgentDB from stored experiences.

Updated Feb 24, 2026
Search Tags:#agentdb#reinforcement learning#q-learning#decision transformer#actor-critic#offline rl#plugin training
One-click install
npx skills add https://github.com/FrekiManagarm/dunlo --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/dunlo/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/FrekiManagarm/dunlo --skill agentdb-learning-plugins-frekimanagarm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you create and train AI reinforcement learning plugins so an agent can improve its decisions from experience instead of relying on fixed rules.

Core Features & Use Cases

  • Reinforcement Learning Plugin Templates (9 algorithms): Use AgentDB’s plugin system to spin up learning strategies such as Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more.
  • Experience-to-Training Workflow: Store training experience patterns, then train a model and evaluate suggested actions with reasoning-based retrieval.
  • Practical Applications: Support offline RL from logged data, safe/on-policy exploration, policy optimization for complex action spaces, and improved agent behavior via experience replay and multi-agent training.

Quick Start

Use the AgentDB CLI to create a learning plugin from the Decision Transformer template for offline reinforcement learning.

Frequently Asked Questions about AgentDB Learning Plugins

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

FAQPage Schema
How do I train reinforcement learning plugins from stored experience data?▼

To train reinforcement learning plugins from stored experience data, you use an experience-to-training workflow that inserts logged trajectories into an AgentDB plugin and retrieves suggested actions for evaluation. This supports offline RL and value-based control.

Can I use offline reinforcement learning to improve agent decision-making from logged trajectories?▼

Yes, you can use offline reinforcement learning to improve agent decision-making from logged trajectories. The system provides plugin templates like the Decision Transformer specifically designed to learn and optimize policies from historical data without active environment interaction.

What reinforcement learning algorithms are available for plugin training?▼

Available reinforcement learning algorithms for plugin training include nine templates such as Decision Transformer, Q-Learning, SARSA, and Actor-Critic. These support value-based control, policy-gradient training, and advanced regimes like experience replay.

Does AgentDB support multi-task and curriculum learning for reinforcement learning agents?▼

Yes, AgentDB supports multi-task and curriculum learning for reinforcement learning agents. The plugin workflow accommodates advanced training regimes, allowing you to progressively scale task complexity and optimize policies across multiple concurrent objectives.

What do I need to set up before building reinforcement learning plugins with AgentDB?▼

Before building reinforcement learning plugins with AgentDB, you need AgentDB v1.0.7 or higher and a plugin workflow that supports creation, experience insertion, training, and retrieval-based evaluation via the adapter. No additional dependencies are required.

When should I use offline reinforcement learning instead of on-policy exploration?▼

You should use offline reinforcement learning instead of on-policy exploration when you want to optimize agent behavior safely from existing logged data. On-policy exploration is better suited for safe, active environment interaction during policy optimization.

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