When Training Rl Agents Use Agentdb Learning

/============================================================================/ /* AGENTDB REINFORCEMENT LEARNING TRAINING SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: AgentDB Reinforcement Learning Training version: 1.0.0 description: | [assert|neutral] AgentDB Reinforcement Learning Training skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags:

• general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Reinforcement Learning Training workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "AgentDB Reinforcement Learning Training", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Reinforcement Learning Training", "agentdb", "workflow"], context: "user needs AgentDB Reinforcement Learning Training capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

AgentDB Reinforcement Learning Training

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Overview

Train AI learning plugins with AgentDB's 9 reinforcement learning algorithms including Decision Transformer, Q-Learning, SARSA, Actor-Critic, PPO, and more. Build self-learning agents, implement RL, and optimize agent behavior through experience.

When to Use This Skill

Use this skill when you need to:

• Train autonomous agents that learn from experience
• Implement reinforcement learning systems
• Optimize agent behavior through trial and error
• Build self-improving AI systems
• Deploy RL agents in production environments
• Benchmark and compare RL algorithms

Available RL Algorithms

1. Q-Learning - Value-based, off-policy
2. SARSA - Value-based, on-policy
3. Deep Q-Network (DQN) - Deep RL with experience replay
4. Actor-Critic - Policy gradient with value baseline
5. Proximal Policy Optimization (PPO) - Trust region policy optimization
6. Decision Transformer - Offline RL with transformers
7. Advantage Actor-Critic (A2C) - Synchronous advantage estimation
8. Twin Delayed DDPG (TD3) - Continuous control
9. Soft Actor-Critic (SAC) - Maximum entropy RL

SOP Framework: 5-Phase RL Training Deployment

Phase 1: Initialize Learning Environment (1-2 hours)

Objective: Setup AgentDB learning infrastructure with environment configuration

Agent: ml-developer

Steps:

1. Install AgentDB Learning Module

npm install agentdb-learning@latest
npm install @agentdb/rl-algorithms @agentdb/environments

2. Initialize learning database

import { AgentDB, LearningPlugin } from 'agentdb-learning';

const learningDB = new AgentDB({
  name: 'rl-training-db',
  dimensions: 512, // State embedding dimension
  learning: {
    enabled: true,
    persistExperience: true,
    replayBufferSize: 100000
  }
});

await learningDB.initialize();

// Create learning plugin
const learningPlugin = new LearningPlugin({
  database: learningDB,
  algorithms: ['q-learning', 'dqn', 'ppo', 'actor-critic'],
  config: {
    batchSize: 64,
    learningRate: 0.001,
    discountFactor: 0.99,
    explorationRate: 1.0,
    explorationDecay: 0.995
  }
});

await learningPlugin.initialize();

3. Define environment

import { Environment } from '@agentdb/environments';

const environment = new Environment({
  name: 'grid-world',
  stateSpace: {
    type: 'continuous',
    shape: [10, 10],
    bounds: [[0, 10], [0, 10]]
  },
  actionSpace: {
    type: 'discrete',
    actions: ['up', 'down', 'left', 'right']
  },
  rewardFunction: (state, action, nextState) => {
    // Distance to goal reward
    const goalDistance = Math.sqrt(
      Math.pow(nextState[0] - 9, 2) +
      Math.pow(nextState[1] - 9, 2)
    );
    return -goalDistance + (goalDistance === 0 ? 100 : 0);
  },
  terminalCondition: (state) => {
    return state[0] === 9 && state[1] === 9; // Reached goal
  }
});

await environment.initialize();

4. Setup monitoring

const monitor = learningPlugin.createMonitor({
  metrics: ['reward', 'loss', 'exploration-rate', 'episode-length'],
  logInterval: 100, // Log every 100 episodes
  saveCheckpoints: true,
  checkpointInterval: 1000
});

monitor.on('episode-complete', (episode) => {
  console.log('Episode:', episode.number, 'Reward:', episode.totalReward);
});

Memory Pattern:

await agentDB.memory.store('agentdb/learning/environment', {
  name: environment.name,
  stateSpace: environment.stateSpace,
  actionSpace: environment.actionSpace,
  initialized: Date.now()
});

Validation:

• Learning database initialized
• Environment configured and tested
• Monitor capturing metrics
• Configuration stored in memory

Phase 2: Configure RL Algorithm (1-2 hours)

Objective: Select and configure RL algorithm for the learning task

Agent: ml-developer

Steps:

1. **Select algo

/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/AgentDB Reinforcement Learning Training/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "AgentDB Reinforcement Learning Training-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/

[commit|confident] AGENTDB REINFORCEMENT LEARNING TRAINING_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]