Agent skill

agentdb-learning-plugins

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.

View SKILL.md on GitHub Repository
Stars 232
Forks 15

Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dnyoussef/agentdb-learning-plugins

SKILL.md

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 agentdb@latest list-plugins

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

# Shows: algorithm, configuration, training status

Quick Start with API

typescript
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');

Available Learning Algorithms (9 Total)

1. Decision Transformer (Recommended)

Type: Offline Reinforcement Learning Best For: Learning from logged experiences, imitation learning Strengths: No online interaction needed, stable training

bash
npx agentdb@latest create-plugin -t decision-transformer -n dt-agent

Use Cases:

  • Learn from historical data
  • Imitation learning from expert demonstrations
  • Safe learning without environment interaction
  • Sequence modeling tasks

Configuration:

json
{
  "algorithm": "decision-transformer",
  "model_size": "base",
  "context_length": 20,
  "embed_dim": 128,
  "n_heads": 8,
  "n_layers": 6
}

2. Q-Learning

Type: Value-Based RL (Off-Policy) Best For: Discrete action spaces, sample efficiency Strengths: Proven, simple, works well for small/medium problems

bash
npx agentdb@latest create-plugin -t q-learning -n q-agent

Use Cases:

  • Grid worlds, board games
  • Navigation tasks
  • Resource allocation
  • Discrete decision-making

Configuration:

json
{
  "algorithm": "q-learning",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1,
  "epsilon_decay": 0.995
}

3. SARSA

Type: Value-Based RL (On-Policy) Best For: Safe exploration, risk-sensitive tasks Strengths: More conservative than Q-Learning, better for safety

bash
npx agentdb@latest create-plugin -t sarsa -n sarsa-agent

Use Cases:

  • Safety-critical applications
  • Risk-sensitive decision-making
  • Online learning with exploration

Configuration:

json
{
  "algorithm": "sarsa",
  "learning_rate": 0.001,
  "gamma": 0.99,
  "epsilon": 0.1
}

4. Actor-Critic

Type: Policy Gradient with Value Baseline Best For: Continuous actions, variance reduction Strengths: Stable, works for continuous/discrete actions

bash
npx agentdb@latest create-plugin -t actor-critic -n ac-agent

Use Cases:

  • Continuous control (robotics, simulations)
  • Complex action spaces
  • Multi-agent coordination

Configuration:

json
{
  "algorithm": "actor-critic",
  "actor_lr": 0.001,
  "critic_lr": 0.002,
  "gamma": 0.99,
  "entropy_coef": 0.01
}

5. Active Learning

Type: Query-Based Learning Best For: Label-efficient learning, human-in-the-loop Strengths: Minimizes labeling cost, focuses on uncertain samples

Use Cases:

  • Human feedback incorporation
  • Label-efficient training
  • Uncertainty sampling
  • Annotation cost reduction

6. Adversarial Training

Type: Robustness Enhancement Best For: Safety, robustness to perturbations Strengths: Improves model robustness, adversarial defense

Use Cases:

  • Security applications
  • Robust decision-making
  • Adversarial defense
  • Safety testing

7. Curriculum Learning

Type: Progressive Difficulty Training Best For: Complex tasks, faster convergence Strengths: Stable learning, faster convergence on hard tasks

Use Cases:

  • Complex multi-stage tasks
  • Hard exploration problems
  • Skill composition
  • Transfer learning

8. Federated Learning

Type: Distributed Learning Best For: Privacy, distributed data Strengths: Privacy-preserving, scalable

Use Cases:

  • Multi-agent systems
  • Privacy-sensitive data
  • Distributed training
  • Collaborative learning

9. Multi-Task Learning

Type: Transfer Learning Best For: Related tasks, knowledge sharing Strengths: Faster learning on new tasks, better generalization

Use Cases:

  • Task families
  • Transfer learning
  • Domain adaptation
  • Meta-learning

Training Workflow

1. Collect Experiences

typescript
// Store experiences during agent execution
for (let i = 0; i < numEpisodes; i++) {
  const episode = runEpisode();

  for (const step of episode.steps) {
    await adapter.insertPattern({
      id: '',
      type: 'experience',
      domain: 'task-domain',
      pattern_data: JSON.stringify({
        embedding: await computeEmbedding(JSON.stringify(step)),
        pattern: {
          state: step.state,
          action: step.action,
          reward: step.reward,
          next_state: step.next_state,
          done: step.done
        }
      }),
      confidence: step.reward > 0 ? 0.9 : 0.5,
      usage_count: 1,
      success_count: step.reward > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }
}

2. Train Model

typescript
// Train on collected experiences
const trainingMetrics = await adapter.train({
  epochs: 100,
  batchSize: 64,
  learningRate: 0.001,
  validationSplit: 0.2,
});

console.log('Training Metrics:', trainingMetrics);
// {
//   loss: 0.023,
//   valLoss: 0.028,
//   duration: 1523,
//   epochs: 100
// }

3. Evaluate Performance

typescript
// Retrieve similar successful experiences
const testQuery = await computeEmbedding(JSON.stringify(testState));
const result = await adapter.retrieveWithReasoning(testQuery, {
  domain: 'task-domain',
  k: 10,
  synthesizeContext: true,
});

// Evaluate action quality
const suggestedAction = result.memories[0].pattern.action;
const confidence = result.memories[0].similarity;

console.log('Suggested Action:', suggestedAction);
console.log('Confidence:', confidence);

Advanced Training Techniques

Experience Replay

typescript
// Store experiences in buffer
const replayBuffer = [];

// Sample random batch for training
const batch = sampleRandomBatch(replayBuffer, batchSize: 32);

// Train on batch
await adapter.train({
  data: batch,
  epochs: 1,
  batchSize: 32,
});

Prioritized Experience Replay

typescript
// Store experiences with priority (TD error)
await adapter.insertPattern({
  // ... standard fields
  confidence: tdError,  // Use TD error as confidence/priority
  // ...
});

// Retrieve high-priority experiences
const highPriority = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'task-domain',
  k: 32,
  minConfidence: 0.7,  // Only high TD-error experiences
});

Multi-Agent Training

typescript
// Collect experiences from multiple agents
for (const agent of agents) {
  const experience = await agent.step();

  await adapter.insertPattern({
    // ... store experience with agent ID
    domain: `multi-agent/${agent.id}`,
  });
}

// Train shared model
await adapter.train({
  epochs: 50,
  batchSize: 64,
});

Performance Optimization

Batch Training

typescript
// Collect batch of experiences
const experiences = collectBatch(size: 1000);

// Batch insert (500x faster)
for (const exp of experiences) {
  await adapter.insertPattern({ /* ... */ });
}

// Train on batch
await adapter.train({
  epochs: 10,
  batchSize: 128,  // Larger batch for efficiency
});

Incremental Learning

typescript
// Train incrementally as new data arrives
setInterval(async () => {
  const newExperiences = getNewExperiences();

  if (newExperiences.length > 100) {
    await adapter.train({
      epochs: 5,
      batchSize: 32,
    });
  }
}, 60000);  // Every minute

Integration with Reasoning Agents

Combine learning with reasoning for better performance:

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

// Use reasoning agents for inference
const result = await adapter.retrieveWithReasoning(queryEmbedding, {
  domain: 'decision-making',
  k: 10,
  useMMR: true,              // Diverse experiences
  synthesizeContext: true,    // Rich context
  optimizeMemory: true,       // Consolidate patterns
});

// Make decision based on learned experiences + reasoning
const decision = result.context.suggestedAction;
const confidence = result.memories[0].similarity;

CLI Operations

bash
# Create plugin
npx agentdb@latest create-plugin -t decision-transformer -n my-plugin

# List plugins
npx agentdb@latest list-plugins

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

# List templates
npx agentdb@latest list-templates

Troubleshooting

Issue: Training not converging

typescript
// Reduce learning rate
await adapter.train({
  epochs: 100,
  batchSize: 32,
  learningRate: 0.0001,  // Lower learning rate
});

Issue: Overfitting

typescript
// Use validation split
await adapter.train({
  epochs: 50,
  batchSize: 64,
  validationSplit: 0.2,  // 20% validation
});

// Enable memory optimization
await adapter.retrieveWithReasoning(queryEmbedding, {
  optimizeMemory: true,  // Consolidate, reduce overfitting
});

Issue: Slow training

bash
# Enable quantization for faster inference
# Use binary quantization (32x faster)

Learn More

Category: Machine Learning / Reinforcement Learning Difficulty: Intermediate to Advanced Estimated Time: 30-60 minutes

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

aiskillstore/marketplace

perigon-backend

Perigon ASP.NET Core + EF Core + Aspire conventions

232 15
Explore
aiskillstore/marketplace

perigon-agent

Pointers for Copilot/agents to apply Perigon conventions

232 15
Explore
aiskillstore/marketplace

perigon-angular

Angular 21+ standalone/Material/signal conventions for Perigon WebApp

232 15
Explore
aiskillstore/marketplace

fastapi-mastery

Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.

232 15
Explore
aiskillstore/marketplace

context7-efficient

Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.

232 15
Explore
aiskillstore/marketplace

browser-use

Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction.

232 15
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results