# When Training Rl Agents Use Agentdb Learning > When Training Rl Agents Use Agentdb Learning - Skill: `dnyoussef/when-training-rl-agents-use-agentdb-learning` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-training-rl-agents-use-agentdb-learning` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-training-rl-agents-use-agentdb-learning/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/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** ```bash npm install agentdb-learning@latest npm install @agentdb/rl-algorithms @agentdb/environments ``` 2. **Initialize learning database** ```typescript 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** ```typescript 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** ```typescript 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:** ```typescript 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]