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AgentDB Learning Plugins

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

11|Updated Jan 20, 2026
Search Tags:#typescript#machine learning#agentdb#agent training#reinforcement learning#q-learning#decision transformer
One-click install
npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill agentdb-learning-plugins-ishandutta2007
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Learning Plugins
Source: https://github.com/ishandutta2007/claude-agent-orchestration/tree/main/.claude/skills/agentdb-learning
Command: npx skills add https://github.com/ishandutta2007/claude-agent-orchestration --skill agentdb-learning-plugins-ishandutta2007

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Building self-improving AI 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: Access Decision Transformer, Q-Learning, SARSA, Actor-Critic, Active Learning, Adversarial Training, Curriculum Learning, Federated Learning, and Multi-Task Learning via CLI templates or the TypeScript API. - Experience-Based Training: Store state-action-reward experiences as vector patterns, then train models with configurable epochs, batch sizes, and validation splits. - Reasoning Integration: Combine learned experiences with reasoning-based retrieval (MMR, context synthesis, memory optimization) for decision-making. - Use Case: Train a game-playing agent by logging episode steps into AgentDB, running adapter.train() over collected experiences, then retrieving suggested actions with confidence scores for new 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. Use --dry-run to preview, or list all templates with npx agentdb@latest list-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. The skill documents configuration for each.

How do I train an AgentDB learning model from agent experiences?▼

Store each state-action-reward step with adapter.insertPattern, then call adapter.train with epochs, batchSize, and optional validationSplit. The method returns metrics including loss and duration.

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 but not strictly required.

Why is my AgentDB training not converging?▼

Lower the learning rate (for example to 0.0001) and increase epochs. For overfitting, add a validationSplit of 0.2 and enable optimizeMemory during retrieval to consolidate patterns.

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