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Skill

AgentDB Learning Plugins

From ruvnet

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

Facts

Repository
ruvnet/ruflo
Status
Actively maintained
Last commit
Source file
.agents/skills/agentdb-learning/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# AgentDB Learning Plugins

## What This Skill Does

Provides access to 9 reinforcement learning algorithms via AgentDB's plugin system. Create, train, and deploy learning plugins for autonomous agents that improve through experience. Includes offline RL (Decision Transformer), value-based learning (Q-Learning), policy gradients (Actor-Critic), and advanced techniques.

**Performance**: Train models 10-100x faster with WASM-accelerated neural inference.

## Prerequisites

- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Basic understanding of reinforcement learning (recommended)

---

## Quick Start with CLI

### Create Learning Plugin

```bash
# Interactive wizard
npx agentdb@latest create-plugin

# Use specific template
npx agentdb@latest create-plugin -t decision-transformer -n my-agent

# Preview without creating
npx agentdb@latest create-plugin -t q-learning --dry-run

# Custom output directory
npx agentdb@latest create-plugin -t actor-critic -o ./plugins
```

### List Available Templates

```bash
# Show all plugin templates
npx agentdb@latest list-templates

# Available templates:
# - decision-transformer (sequence modeling RL - recommended)
# - q-learning (value-based learning)
# - sarsa (on-policy TD learning)
# - actor-critic (policy gradient with baseline)
# - curiosity-driven (exploration-based)
```

### Manage Plugins

```bash
# List installed plugins
npx agentd
View full source on GitHub →

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