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Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fruvnet\u002Fagentdb-learning-plugins\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"plugins","learning","AgentDB Learning Plugins","create","reinforcement learning","critic","decision","behavior","use","rl","train","self","algorithms","reinforcement","Transformer","with","2026-11-10 10:12:30",false,"---\nname: agentdb-learning-plugins\ndescription: \"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.\"\n---\n\n# AgentDB Learning Plugins\n\n## What This Skill Does\n\nProvides 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.\n\n**Performance**: Train models 10-100x faster with WASM-accelerated neural inference.\n\n## Prerequisites\n\n- Node.js 18+\n- AgentDB v1.0.7+ (via agentic-flow)\n- Basic understanding of reinforcement learning (recommended)\n\n---\n\n## Quick Start with CLI\n\n### Create Learning Plugin\n\n```bash\n# Interactive wizard\nnpx agentdb@latest create-plugin\n\n# Use specific template\nnpx agentdb@latest create-plugin -t decision-transformer -n my-agent\n\n# Preview without creating\nnpx agentdb@latest create-plugin -t q-learning --dry-run\n\n# Custom output directory\nnpx agentdb@latest create-plugin -t actor-critic -o .\u002Fplugins\n```\n\n### List Available Templates\n\n```bash\n# Show all plugin templates\nnpx agentdb@latest list-templates\n\n# Available templates:\n# - decision-transformer (sequence modeling RL - recommended)\n# - q-learning (value-based learning)\n# - sarsa (on-policy TD learning)\n# - actor-critic (policy gradient with baseline)\n# - curiosity-driven (exploration-based)\n```\n\n### Manage Plugins\n\n```bash\n# List installed plugins\nnpx agentdb@latest list-plugins\n\n# Get plugin information\nnpx agentdb@latest plugin-info my-agent\n\n# Shows: algorithm, configuration, training status\n```\n\n---\n\n## Quick Start with API\n\n```typescript\nimport { createAgentDBAdapter } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize with learning enabled\nconst adapter = await createAgentDBAdapter({\n  dbPath: '.agentdb\u002Flearning.db',\n  enableLearning: true,       \u002F\u002F Enable learning plugins\n  enableReasoning: true,\n  cacheSize: 1000,\n});\n\n\u002F\u002F Store training experience\nawait adapter.insertPattern({\n  id: '',\n  type: 'experience',\n  domain: 'game-playing',\n  pattern_data: JSON.stringify({\n    embedding: await computeEmbedding('state-action-reward'),\n    pattern: {\n      state: [0.1, 0.2, 0.3],\n      action: 2,\n      reward: 1.0,\n      next_state: [0.15, 0.25, 0.35],\n      done: false\n    }\n  }),\n  confidence: 0.9,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n\n\u002F\u002F Train learning model\nconst metrics = await adapter.train({\n  epochs: 50,\n  batchSize: 32,\n});\n\nconsole.log('Training Loss:', metrics.loss);\nconsole.log('Duration:', metrics.duration, 'ms');\n```\n\n---\n\n## Available Learning Algorithms (9 Total)\n\n### 1. Decision Transformer (Recommended)\n\n**Type**: Offline Reinforcement Learning\n**Best For**: Learning from logged experiences, imitation learning\n**Strengths**: No online interaction needed, stable training\n\n```bash\nnpx agentdb@latest create-plugin -t decision-transformer -n dt-agent\n```\n\n**Use Cases**:\n- Learn from historical data\n- Imitation learning from expert demonstrations\n- Safe learning without environment interaction\n- Sequence modeling tasks\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"decision-transformer\",\n  \"model_size\": \"base\",\n  \"context_length\": 20,\n  \"embed_dim\": 128,\n  \"n_heads\": 8,\n  \"n_layers\": 6\n}\n```\n\n### 2. Q-Learning\n\n**Type**: Value-Based RL (Off-Policy)\n**Best For**: Discrete action spaces, sample efficiency\n**Strengths**: Proven, simple, works well for small\u002Fmedium problems\n\n```bash\nnpx agentdb@latest create-plugin -t q-learning -n q-agent\n```\n\n**Use Cases**:\n- Grid worlds, board games\n- Navigation tasks\n- Resource allocation\n- Discrete decision-making\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"q-learning\",\n  \"learning_rate\": 0.001,\n  \"gamma\": 0.99,\n  \"epsilon\": 0.1,\n  \"epsilon_decay\": 0.995\n}\n```\n\n### 3. SARSA\n\n**Type**: Value-Based RL (On-Policy)\n**Best For**: Safe exploration, risk-sensitive tasks\n**Strengths**: More conservative than Q-Learning, better for safety\n\n```bash\nnpx agentdb@latest create-plugin -t sarsa -n sarsa-agent\n```\n\n**Use Cases**:\n- Safety-critical applications\n- Risk-sensitive decision-making\n- Online learning with exploration\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"sarsa\",\n  \"learning_rate\": 0.001,\n  \"gamma\": 0.99,\n  \"epsilon\": 0.1\n}\n```\n\n### 4. Actor-Critic\n\n**Type**: Policy Gradient with Value Baseline\n**Best For**: Continuous actions, variance reduction\n**Strengths**: Stable, works for continuous\u002Fdiscrete actions\n\n```bash\nnpx agentdb@latest create-plugin -t actor-critic -n ac-agent\n```\n\n**Use Cases**:\n- Continuous control (robotics, simulations)\n- Complex action spaces\n- Multi-agent coordination\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"actor-critic\",\n  \"actor_lr\": 0.001,\n  \"critic_lr\": 0.002,\n  \"gamma\": 0.99,\n  \"entropy_coef\": 0.01\n}\n```\n\n### 5. Active Learning\n\n**Type**: Query-Based Learning\n**Best For**: Label-efficient learning, human-in-the-loop\n**Strengths**: Minimizes labeling cost, focuses on uncertain samples\n\n**Use Cases**:\n- Human feedback incorporation\n- Label-efficient training\n- Uncertainty sampling\n- Annotation cost reduction\n\n### 6. Adversarial Training\n\n**Type**: Robustness Enhancement\n**Best For**: Safety, robustness to perturbations\n**Strengths**: Improves model robustness, adversarial defense\n\n**Use Cases**:\n- Security applications\n- Robust decision-making\n- Adversarial defense\n- Safety testing\n\n### 7. Curriculum Learning\n\n**Type**: Progressive Difficulty Training\n**Best For**: Complex tasks, faster convergence\n**Strengths**: Stable learning, faster convergence on hard tasks\n\n**Use Cases**:\n- Complex multi-stage tasks\n- Hard exploration problems\n- Skill composition\n- Transfer learning\n\n### 8. Federated Learning\n\n**Type**: Distributed Learning\n**Best For**: Privacy, distributed data\n**Strengths**: Privacy-preserving, scalable\n\n**Use Cases**:\n- Multi-agent systems\n- Privacy-sensitive data\n- Distributed training\n- Collaborative learning\n\n### 9. Multi-Task Learning\n\n**Type**: Transfer Learning\n**Best For**: Related tasks, knowledge sharing\n**Strengths**: Faster learning on new tasks, better generalization\n\n**Use Cases**:\n- Task families\n- Transfer learning\n- Domain adaptation\n- Meta-learning\n\n---\n\n## Training Workflow\n\n### 1. Collect Experiences\n\n```typescript\n\u002F\u002F Store experiences during agent execution\nfor (let i = 0; i \u003C numEpisodes; i++) {\n  const episode = runEpisode();\n\n  for (const step of episode.steps) {\n    await adapter.insertPattern({\n      id: '',\n      type: 'experience',\n      domain: 'task-domain',\n      pattern_data: JSON.stringify({\n        embedding: await computeEmbedding(JSON.stringify(step)),\n        pattern: {\n          state: step.state,\n          action: step.action,\n          reward: step.reward,\n          next_state: step.next_state,\n          done: step.done\n        }\n      }),\n      confidence: step.reward > 0 ? 0.9 : 0.5,\n      usage_count: 1,\n      success_count: step.reward > 0 ? 1 : 0,\n      created_at: Date.now(),\n      last_used: Date.now(),\n    });\n  }\n}\n```\n\n### 2. Train Model\n\n```typescript\n\u002F\u002F Train on collected experiences\nconst trainingMetrics = await adapter.train({\n  epochs: 100,\n  batchSize: 64,\n  learningRate: 0.001,\n  validationSplit: 0.2,\n});\n\nconsole.log('Training Metrics:', trainingMetrics);\n\u002F\u002F {\n\u002F\u002F   loss: 0.023,\n\u002F\u002F   valLoss: 0.028,\n\u002F\u002F   duration: 1523,\n\u002F\u002F   epochs: 100\n\u002F\u002F }\n```\n\n### 3. Evaluate Performance\n\n```typescript\n\u002F\u002F Retrieve similar successful experiences\nconst testQuery = await computeEmbedding(JSON.stringify(testState));\nconst result = await adapter.retrieveWithReasoning(testQuery, {\n  domain: 'task-domain',\n  k: 10,\n  synthesizeContext: true,\n});\n\n\u002F\u002F Evaluate action quality\nconst suggestedAction = result.memories[0].pattern.action;\nconst confidence = result.memories[0].similarity;\n\nconsole.log('Suggested Action:', suggestedAction);\nconsole.log('Confidence:', confidence);\n```\n\n---\n\n## Advanced Training Techniques\n\n### Experience Replay\n\n```typescript\n\u002F\u002F Store experiences in buffer\nconst replayBuffer = [];\n\n\u002F\u002F Sample random batch for training\nconst batch = sampleRandomBatch(replayBuffer, batchSize: 32);\n\n\u002F\u002F Train on batch\nawait adapter.train({\n  data: batch,\n  epochs: 1,\n  batchSize: 32,\n});\n```\n\n### Prioritized Experience Replay\n\n```typescript\n\u002F\u002F Store experiences with priority (TD error)\nawait adapter.insertPattern({\n  \u002F\u002F ... standard fields\n  confidence: tdError,  \u002F\u002F Use TD error as confidence\u002Fpriority\n  \u002F\u002F ...\n});\n\n\u002F\u002F Retrieve high-priority experiences\nconst highPriority = await adapter.retrieveWithReasoning(queryEmbedding, {\n  domain: 'task-domain',\n  k: 32,\n  minConfidence: 0.7,  \u002F\u002F Only high TD-error experiences\n});\n```\n\n### Multi-Agent Training\n\n```typescript\n\u002F\u002F Collect experiences from multiple agents\nfor (const agent of agents) {\n  const experience = await agent.step();\n\n  await adapter.insertPattern({\n    \u002F\u002F ... store experience with agent ID\n    domain: `multi-agent\u002F${agent.id}`,\n  });\n}\n\n\u002F\u002F Train shared model\nawait adapter.train({\n  epochs: 50,\n  batchSize: 64,\n});\n```\n\n---\n\n## Performance Optimization\n\n### Batch Training\n\n```typescript\n\u002F\u002F Collect batch of experiences\nconst experiences = collectBatch(size: 1000);\n\n\u002F\u002F Batch insert (500x faster)\nfor (const exp of experiences) {\n  await adapter.insertPattern({ \u002F* ... *\u002F });\n}\n\n\u002F\u002F Train on batch\nawait adapter.train({\n  epochs: 10,\n  batchSize: 128,  \u002F\u002F Larger batch for efficiency\n});\n```\n\n### Incremental Learning\n\n```typescript\n\u002F\u002F Train incrementally as new data arrives\nsetInterval(async () => {\n  const newExperiences = getNewExperiences();\n\n  if (newExperiences.length > 100) {\n    await adapter.train({\n      epochs: 5,\n      batchSize: 32,\n    });\n  }\n}, 60000);  \u002F\u002F Every minute\n```\n\n---\n\n## Integration with Reasoning Agents\n\nCombine learning with reasoning for better performance:\n\n```typescript\n\u002F\u002F Train learning model\nawait adapter.train({ epochs: 50, batchSize: 32 });\n\n\u002F\u002F Use reasoning agents for inference\nconst result = await adapter.retrieveWithReasoning(queryEmbedding, {\n  domain: 'decision-making',\n  k: 10,\n  useMMR: true,              \u002F\u002F Diverse experiences\n  synthesizeContext: true,    \u002F\u002F Rich context\n  optimizeMemory: true,       \u002F\u002F Consolidate patterns\n});\n\n\u002F\u002F Make decision based on learned experiences + reasoning\nconst decision = result.context.suggestedAction;\nconst confidence = result.memories[0].similarity;\n```\n\n---\n\n## CLI Operations\n\n```bash\n# Create plugin\nnpx agentdb@latest create-plugin -t decision-transformer -n my-plugin\n\n# List plugins\nnpx agentdb@latest list-plugins\n\n# Get plugin info\nnpx agentdb@latest plugin-info my-plugin\n\n# List templates\nnpx agentdb@latest list-templates\n```\n\n---\n\n## Troubleshooting\n\n### Issue: Training not converging\n```typescript\n\u002F\u002F Reduce learning rate\nawait adapter.train({\n  epochs: 100,\n  batchSize: 32,\n  learningRate: 0.0001,  \u002F\u002F Lower learning rate\n});\n```\n\n### Issue: Overfitting\n```typescript\n\u002F\u002F Use validation split\nawait adapter.train({\n  epochs: 50,\n  batchSize: 64,\n  validationSplit: 0.2,  \u002F\u002F 20% validation\n});\n\n\u002F\u002F Enable memory optimization\nawait adapter.retrieveWithReasoning(queryEmbedding, {\n  optimizeMemory: true,  \u002F\u002F Consolidate, reduce overfitting\n});\n```\n\n### Issue: Slow training\n```bash\n# Enable quantization for faster inference\n# Use binary quantization (32x faster)\n```\n\n---\n\n## Learn More\n\n- **Algorithm Papers**: See docs\u002Falgorithms\u002F for detailed papers\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagentic-flow\u002Ftree\u002Fmain\u002Fpackages\u002Fagentdb\n- **MCP Integration**: `npx agentdb@latest mcp`\n- **Website**: https:\u002F\u002Fagentdb.ruv.io\n\n---\n\n**Category**: Machine Learning \u002F Reinforcement Learning\n**Difficulty**: Intermediate to Advanced\n**Estimated Time**: 30-60 minutes\n","# AgentDB Learning Plugins\n\n## What This Skill Does\n\nProvides 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.\n\n**Performance**: Train models 10-100x faster with WASM-accelerated neural inference.\n\n## Prerequisites\n\n- Node.js 18+\n- AgentDB v1.0.7+ (via agentic-flow)\n- Basic understanding of reinforcement learning (recommended)\n\n---\n\n## Quick Start with CLI\n\n### Create Learning Plugin\n\n```bash\n# Interactive wizard\nnpx agentdb@latest create-plugin\n\n# Use specific template\nnpx agentdb@latest create-plugin -t decision-transformer -n my-agent\n\n# Preview without creating\nnpx agentdb@latest create-plugin -t q-learning --dry-run\n\n# Custom output directory\nnpx agentdb@latest create-plugin -t actor-critic -o .\u002Fplugins\n```\n\n### List Available Templates\n\n```bash\n# Show all plugin templates\nnpx agentdb@latest list-templates\n\n# Available templates:\n# - decision-transformer (sequence modeling RL - recommended)\n# - q-learning (value-based learning)\n# - sarsa (on-policy TD learning)\n# - actor-critic (policy gradient with baseline)\n# - curiosity-driven (exploration-based)\n```\n\n### Manage Plugins\n\n```bash\n# List installed plugins\nnpx agentdb@latest list-plugins\n\n# Get plugin information\nnpx agentdb@latest plugin-info my-agent\n\n# Shows: algorithm, configuration, training status\n```\n\n---\n\n## Quick Start with API\n\n```typescript\nimport { createAgentDBAdapter } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize with learning enabled\nconst adapter = await createAgentDBAdapter({\n  dbPath: '.agentdb\u002Flearning.db',\n  enableLearning: true,       \u002F\u002F Enable learning plugins\n  enableReasoning: true,\n  cacheSize: 1000,\n});\n\n\u002F\u002F Store training experience\nawait adapter.insertPattern({\n  id: '',\n  type: 'experience',\n  domain: 'game-playing',\n  pattern_data: JSON.stringify({\n    embedding: await computeEmbedding('state-action-reward'),\n    pattern: {\n      state: [0.1, 0.2, 0.3],\n      action: 2,\n      reward: 1.0,\n      next_state: [0.15, 0.25, 0.35],\n      done: false\n    }\n  }),\n  confidence: 0.9,\n  usage_count: 1,\n  success_count: 1,\n  created_at: Date.now(),\n  last_used: Date.now(),\n});\n\n\u002F\u002F Train learning model\nconst metrics = await adapter.train({\n  epochs: 50,\n  batchSize: 32,\n});\n\nconsole.log('Training Loss:', metrics.loss);\nconsole.log('Duration:', metrics.duration, 'ms');\n```\n\n---\n\n## Available Learning Algorithms (9 Total)\n\n### 1. Decision Transformer (Recommended)\n\n**Type**: Offline Reinforcement Learning\n**Best For**: Learning from logged experiences, imitation learning\n**Strengths**: No online interaction needed, stable training\n\n```bash\nnpx agentdb@latest create-plugin -t decision-transformer -n dt-agent\n```\n\n**Use Cases**:\n- Learn from historical data\n- Imitation learning from expert demonstrations\n- Safe learning without environment interaction\n- Sequence modeling tasks\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"decision-transformer\",\n  \"model_size\": \"base\",\n  \"context_length\": 20,\n  \"embed_dim\": 128,\n  \"n_heads\": 8,\n  \"n_layers\": 6\n}\n```\n\n### 2. Q-Learning\n\n**Type**: Value-Based RL (Off-Policy)\n**Best For**: Discrete action spaces, sample efficiency\n**Strengths**: Proven, simple, works well for small\u002Fmedium problems\n\n```bash\nnpx agentdb@latest create-plugin -t q-learning -n q-agent\n```\n\n**Use Cases**:\n- Grid worlds, board games\n- Navigation tasks\n- Resource allocation\n- Discrete decision-making\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"q-learning\",\n  \"learning_rate\": 0.001,\n  \"gamma\": 0.99,\n  \"epsilon\": 0.1,\n  \"epsilon_decay\": 0.995\n}\n```\n\n### 3. SARSA\n\n**Type**: Value-Based RL (On-Policy)\n**Best For**: Safe exploration, risk-sensitive tasks\n**Strengths**: More conservative than Q-Learning, better for safety\n\n```bash\nnpx agentdb@latest create-plugin -t sarsa -n sarsa-agent\n```\n\n**Use Cases**:\n- Safety-critical applications\n- Risk-sensitive decision-making\n- Online learning with exploration\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"sarsa\",\n  \"learning_rate\": 0.001,\n  \"gamma\": 0.99,\n  \"epsilon\": 0.1\n}\n```\n\n### 4. Actor-Critic\n\n**Type**: Policy Gradient with Value Baseline\n**Best For**: Continuous actions, variance reduction\n**Strengths**: Stable, works for continuous\u002Fdiscrete actions\n\n```bash\nnpx agentdb@latest create-plugin -t actor-critic -n ac-agent\n```\n\n**Use Cases**:\n- Continuous control (robotics, simulations)\n- Complex action spaces\n- Multi-agent coordination\n\n**Configuration**:\n```json\n{\n  \"algorithm\": \"actor-critic\",\n  \"actor_lr\": 0.001,\n  \"critic_lr\": 0.002,\n  \"gamma\": 0.99,\n  \"entropy_coef\": 0.01\n}\n```\n\n### 5. Active Learning\n\n**Type**: Query-Based Learning\n**Best For**: Label-efficient learning, human-in-the-loop\n**Strengths**: Minimizes labeling cost, focuses on uncertain samples\n\n**Use Cases**:\n- Human feedback incorporation\n- Label-efficient training\n- Uncertainty sampling\n- Annotation cost reduction\n\n### 6. Adversarial Training\n\n**Type**: Robustness Enhancement\n**Best For**: Safety, robustness to perturbations\n**Strengths**: Improves model robustness, adversarial defense\n\n**Use Cases**:\n- Security applications\n- Robust decision-making\n- Adversarial defense\n- Safety testing\n\n### 7. Curriculum Learning\n\n**Type**: Progressive Difficulty Training\n**Best For**: Complex tasks, faster convergence\n**Strengths**: Stable learning, faster convergence on hard tasks\n\n**Use Cases**:\n- Complex multi-stage tasks\n- Hard exploration problems\n- Skill composition\n- Transfer learning\n\n### 8. Federated Learning\n\n**Type**: Distributed Learning\n**Best For**: Privacy, distributed data\n**Strengths**: Privacy-preserving, scalable\n\n**Use Cases**:\n- Multi-agent systems\n- Privacy-sensitive data\n- Distributed training\n- Collaborative learning\n\n### 9. Multi-Task Learning\n\n**Type**: Transfer Learning\n**Best For**: Related tasks, knowledge sharing\n**Strengths**: Faster learning on new tasks, better generalization\n\n**Use Cases**:\n- Task families\n- Transfer learning\n- Domain adaptation\n- Meta-learning\n\n---\n\n## Training Workflow\n\n### 1. Collect Experiences\n\n```typescript\n\u002F\u002F Store experiences during agent execution\nfor (let i = 0; i \u003C numEpisodes; i++) {\n  const episode = runEpisode();\n\n  for (const step of episode.steps) {\n    await adapter.insertPattern({\n      id: '',\n      type: 'experience',\n      domain: 'task-domain',\n      pattern_data: JSON.stringify({\n        embedding: await computeEmbedding(JSON.stringify(step)),\n        pattern: {\n          state: step.state,\n          action: step.action,\n          reward: step.reward,\n          next_state: step.next_state,\n          done: step.done\n        }\n      }),\n      confidence: step.reward > 0 ? 0.9 : 0.5,\n      usage_count: 1,\n      success_count: step.reward > 0 ? 1 : 0,\n      created_at: Date.now(),\n      last_used: Date.now(),\n    });\n  }\n}\n```\n\n### 2. Train Model\n\n```typescript\n\u002F\u002F Train on collected experiences\nconst trainingMetrics = await adapter.train({\n  epochs: 100,\n  batchSize: 64,\n  learningRate: 0.001,\n  validationSplit: 0.2,\n});\n\nconsole.log('Training Metrics:', trainingMetrics);\n\u002F\u002F {\n\u002F\u002F   loss: 0.023,\n\u002F\u002F   valLoss: 0.028,\n\u002F\u002F   duration: 1523,\n\u002F\u002F   epochs: 100\n\u002F\u002F }\n```\n\n### 3. Evaluate Performance\n\n```typescript\n\u002F\u002F Retrieve similar successful experiences\nconst testQuery = await computeEmbedding(JSON.stringify(testState));\nconst result = await adapter.retrieveWithReasoning(testQuery, {\n  domain: 'task-domain',\n  k: 10,\n  synthesizeContext: true,\n});\n\n\u002F\u002F Evaluate action quality\nconst suggestedAction = result.memories[0].pattern.action;\nconst confidence = result.memories[0].similarity;\n\nconsole.log('Suggested Action:', suggestedAction);\nconsole.log('Confidence:', confidence);\n```\n\n---\n\n## Advanced Training Techniques\n\n### Experience Replay\n\n```typescript\n\u002F\u002F Store experiences in buffer\nconst replayBuffer = [];\n\n\u002F\u002F Sample random batch for training\nconst batch = sampleRandomBatch(replayBuffer, batchSize: 32);\n\n\u002F\u002F Train on batch\nawait adapter.train({\n  data: batch,\n  epochs: 1,\n  batchSize: 32,\n});\n```\n\n### Prioritized Experience Replay\n\n```typescript\n\u002F\u002F Store experiences with priority (TD error)\nawait adapter.insertPattern({\n  \u002F\u002F ... standard fields\n  confidence: tdError,  \u002F\u002F Use TD error as confidence\u002Fpriority\n  \u002F\u002F ...\n});\n\n\u002F\u002F Retrieve high-priority experiences\nconst highPriority = await adapter.retrieveWithReasoning(queryEmbedding, {\n  domain: 'task-domain',\n  k: 32,\n  minConfidence: 0.7,  \u002F\u002F Only high TD-error experiences\n});\n```\n\n### Multi-Agent Training\n\n```typescript\n\u002F\u002F Collect experiences from multiple agents\nfor (const agent of agents) {\n  const experience = await agent.step();\n\n  await adapter.insertPattern({\n    \u002F\u002F ... store experience with agent ID\n    domain: `multi-agent\u002F${agent.id}`,\n  });\n}\n\n\u002F\u002F Train shared model\nawait adapter.train({\n  epochs: 50,\n  batchSize: 64,\n});\n```\n\n---\n\n## Performance Optimization\n\n### Batch Training\n\n```typescript\n\u002F\u002F Collect batch of experiences\nconst experiences = collectBatch(size: 1000);\n\n\u002F\u002F Batch insert (500x faster)\nfor (const exp of experiences) {\n  await adapter.insertPattern({ \u002F* ... *\u002F });\n}\n\n\u002F\u002F Train on batch\nawait adapter.train({\n  epochs: 10,\n  batchSize: 128,  \u002F\u002F Larger batch for efficiency\n});\n```\n\n### Incremental Learning\n\n```typescript\n\u002F\u002F Train incrementally as new data arrives\nsetInterval(async () => {\n  const newExperiences = getNewExperiences();\n\n  if (newExperiences.length > 100) {\n    await adapter.train({\n      epochs: 5,\n      batchSize: 32,\n    });\n  }\n}, 60000);  \u002F\u002F Every minute\n```\n\n---\n\n## Integration with Reasoning Agents\n\nCombine learning with reasoning for better performance:\n\n```typescript\n\u002F\u002F Train learning model\nawait adapter.train({ epochs: 50, batchSize: 32 });\n\n\u002F\u002F Use reasoning agents for inference\nconst result = await adapter.retrieveWithReasoning(queryEmbedding, {\n  domain: 'decision-making',\n  k: 10,\n  useMMR: true,              \u002F\u002F Diverse experiences\n  synthesizeContext: true,    \u002F\u002F Rich context\n  optimizeMemory: true,       \u002F\u002F Consolidate patterns\n});\n\n\u002F\u002F Make decision based on learned experiences + reasoning\nconst decision = result.context.suggestedAction;\nconst confidence = result.memories[0].similarity;\n```\n\n---\n\n## CLI Operations\n\n```bash\n# Create plugin\nnpx agentdb@latest create-plugin -t decision-transformer -n my-plugin\n\n# List plugins\nnpx agentdb@latest list-plugins\n\n# Get plugin info\nnpx agentdb@latest plugin-info my-plugin\n\n# List templates\nnpx agentdb@latest list-templates\n```\n\n---\n\n## Troubleshooting\n\n### Issue: Training not converging\n```typescript\n\u002F\u002F Reduce learning rate\nawait adapter.train({\n  epochs: 100,\n  batchSize: 32,\n  learningRate: 0.0001,  \u002F\u002F Lower learning rate\n});\n```\n\n### Issue: Overfitting\n```typescript\n\u002F\u002F Use validation split\nawait adapter.train({\n  epochs: 50,\n  batchSize: 64,\n  validationSplit: 0.2,  \u002F\u002F 20% validation\n});\n\n\u002F\u002F Enable memory optimization\nawait adapter.retrieveWithReasoning(queryEmbedding, {\n  optimizeMemory: true,  \u002F\u002F Consolidate, reduce overfitting\n});\n```\n\n### Issue: Slow training\n```bash\n# Enable quantization for faster inference\n# Use binary quantization (32x faster)\n```\n\n---\n\n## Learn More\n\n- **Algorithm Papers**: See docs\u002Falgorithms\u002F for detailed papers\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fruvnet\u002Fagentic-flow\u002Ftree\u002Fmain\u002Fpackages\u002Fagentdb\n- **MCP Integration**: `npx agentdb@latest mcp`\n- **Website**: https:\u002F\u002Fagentdb.ruv.io\n\n---\n\n**Category**: Machine Learning \u002F Reinforcement Learning\n**Difficulty**: Intermediate to Advanced\n**Estimated Time**: 30-60 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