agentdb-learning-plugins

What this skill does

# 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