skills.rest
wandreandrade2018-maker
wandreandrade2018-makerCOMMUNITY
Explore all skills by this author →
GitHub

AgentDB Learning Plugins

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

Updated Aug 18, 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/wandreandrade2018-maker/claude-config --skill agentdb-learning-plugins-wandreandrade2018-maker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/wandreandrade2018-maker/claude-config/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill agentdb-learning-plugins-wandreandrade2018-maker

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-made templates and APIs for training agents that learn from experience using AgentDB's plugin system. ## Core Features & Use Cases - Nine RL Algorithms: Decision Transformer, Q-Learning, SARSA, Actor-Critic, plus active, adversarial, curriculum, federated, 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 logging state-action-reward tuples during episodes, then running batch training so the agent suggests high-confidence actions for new states. ## Quick Start Ask the AI to create a Decision Transformer learning plugin with AgentDB and train it on your collected 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. An interactive wizard is available without flags, and --dry-run previews the output before creating files.

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

Decision Transformer suits offline learning from logged experiences and is the recommended default. Q-Learning fits discrete action spaces, SARSA fits risk-sensitive tasks, and Actor-Critic handles continuous action spaces.

What are the requirements for using AgentDB learning plugins?▼

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

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. For overfitting, enable a validation split of 0.2 and use memory optimization during retrieval to consolidate patterns.

Can I train multiple agents with a shared AgentDB model?▼

Yes, store each agent's experiences under a per-agent domain such as multi-agent/agent-id, then run a single shared training pass over the collected data. This supports federated and multi-agent learning workflows.

Related Skills

Explore More Software Engineering Skills→
coco-researchcoco-research
264

agent-lightning

Train and optimize AI agents with reinforcement learning and prompt optimization.

Community
Advanced
RyanJarvRyanJarv

AgentDB Learning Plugins

Create and train reinforcement learning plugins for autonomous agents.

Community
Advanced
goodnight000goodnight000

memory-systems

Persist agent state across sessions using vector memories and a temporal knowledge graph.

Community
Intermediate
Orchestra-ResearchOrchestra-Research
11.5k

fine-tuning-with-trl

Fine-tunes LLMs using TRL with SFT, DPO, PPO, and GRPO.

Official
Advanced