AgentDB Learning Plugins

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)

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Quick Start with CLI

Create Learning Plugin

# 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

# 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

# List installed plugins
npx agentdb@latest list-plugins

# Get plugin information
npx agentdb@latest plugin-info my-agent

# Shows: algorithm, configuration, training status

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Quick Start with API

import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';

// Initialize with learning enabled
const adapter = await createAgentDBAdapter({
  dbPath: '.agentdb/learning.db',
  enableLearning: true,       // Enable learning plugins
  enableReasoning: true,
  cacheSize: 1000,
});

// Store training experience
await adapter.insertPattern({
  id: '',
  type: 'experience',
  domain: 'game-playing',
  pattern_data: JSON.stringify({
    embedding: await computeEmbedding('state-action-reward'),
    pattern: {
      state: [0.1, 0.2, 0.3],
      action: 2,
      reward: 1.0,
      next_state: [0.15, 0.25, 0.35],
      done: false
    }
  }),
  confidence: 0.9,
  usage_count: 1,
  success_count: 1,
  created_at: Date.now(),
  last_used: Date.now(),
});

// Train learning model
const metrics = await adapter.train({
  epochs: 50,
  batchSize: 32,
});

console.log('Training Loss:', metrics.loss);
console.log('Duration:', metrics.duration, 'ms');