# Create RAN RL workspace
mkdir -p ran-rl/{agents,environments,policies,experience}
cd ran-rl

# Initialize AgentDB for RL experience replay
npx agentdb@latest init ./.agentdb/ran-rl.db --dimension 1536

# Install RL packages
npm init -y
npm install agentdb @tensorflow/tfjs-node
npm install gym-js
npm install multi-objective-rl

import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank';

class RANRLAgent {
  private agentDB: AgentDBAdapter;
  private policyNetwork: any; // TensorFlow.js model
  private valueNetwork: any;  // Value function approximator
  private experienceBuffer: Experience[];
  private epsilon: number = 1.0; // Exploration rate

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-rl.db',
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 2500, // Larger cache for experience replay
    });

    await this.buildNetworks();
    await this.loadExperiencesFromAgentDB();
    this.experienceBuffer = [];
  }

  private async buildNetworks() {
    // Policy Network (Actor)
    this.policyNetwork = tf.sequential({
      layers: [
        tf.layers.dense({ inputShape: [12], units: 128, activation: 'relu' }),
        tf.layers.dense({ units: 64, activation: 'relu' }),
        tf.layers.dense({ units: 32, activation: 'relu' }),
        tf.layers.dense({ units: 8, activation: 'softmax' }) // 8 RAN actions
      ]
    });

    // Value Network (Critic)
    this.valueNetwork = tf.sequential({
      layers: [
        tf.layers.dense({ inputShape: [12], units: 128, activation: 'relu' }),
        tf.layers.dense({ units: 64, activation: 'relu' }),
        tf.layers.dense({ units: 32, activation: 'relu' }),
        tf.layers.dense({ units: 1, activation: 'linear' })
      ]
    });

    // Compile networks
    this.policyNetwork.compile({
      optimizer: tf.train.adam(0.0001),
      loss: 'categoricalCrossentropy'
    });

    this.valueNetwork.compile({
      optimizer: tf.train.adam(0.001),
      loss: 'meanSquaredError'
    });
  }

  async selectAction(state: RANState): Promise<number> {
    const stateTensor = this.encodeState(state);

    // Epsilon-greedy exploration
    if (Math.random() < this.epsilon) {
      return Math.floor(Math.random() * 8); // Random action
    }

    // Use policy network
    const actionProbs = this.policyNetwork.predict(stateTensor) as tf.Tensor;
    const action = await tf.argMax(actionProbs, 1).data();

    stateTensor.dispose();
    actionProbs.dispose();

    return action[0];
  }

  private encodeState(state: RANState): tf.Tensor {
    // Normalize and encode RAN state
    const encoded = [
      state.throughput / 1000,        // Normalized throughput (0-1)
      state.latency / 100,           // Normalized latency (0-1)
      state.packetLoss,               // Packet loss (0-1)
      state.signalStrength / 100,     // Normalized signal strength
      state.interference,             // Interference (0-1)
      state.energyConsumption / 200,  // Normalized energy consumption
      state.userCount / 100,         // Normalized user count
      state.mobilityIndex / 100,     // Normalized mobility index
      state.coverageHoleCount / 50,  // Normalized coverage holes
      Math.sin(Date.now() / 3600000), // Time of day
      Math.cos(Date.now() / 3600000), // Time of day
      Math.random()                    // Exploration factor
    ];

    return tf.tensor2d([encoded]);
  }

  async storeExperience(state: RANState, action: number, reward: number, nextState: RANState, done: boolean) {
    const experience: Experience = {
      state: this.encodeState(state),
      action,
      reward,
      nextState: this.encodeState(nextState),
      done,
      timestamp: Date.now()
    };

    // Store in local buffer
    this.experienceBuffer.push(experience);

    // Store in AgentDB for long-term memory
    await this.storeExperienceInAgentDB(experience);

    // Maintain buffer size
    if (this.experienceBuffer.length > 10000) {
      this.experienceBuffer.shift();
    }
  }

  private async storeExperienceInAgentDB(experience: Experience) {
    const experienceData = {
      stateVector: Array.from((await experience.state.data()) as Float32Array),
      action: experience.action,
      reward: experience.reward,
      nextStateVector: Array.from((await experience.nextState.data()) as Float32Array),
      done: experience.done,
      timestamp: experience.timestamp
    };

    const embedding = await computeEmbedding(JSON.stringify(experienceData));

    await this.agentDB.insertPattern({
      id: '',
      type: 'rl-experience',
      domain: 'ran-reinforcement-learning',
      pattern_data: JSON.stringify({ embedding, pattern: experienceData }),
      confidence: Math.min(Math.abs(experience.reward), 1.0),
      usage_count: 1,
      success_count: experience.reward > 0 ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  async trainStep(): Promise<TrainingMetrics> {
    if (this.experienceBuffer.length < 32) {
      return { loss: 0, policyLoss: 0, valueLoss: 0 };
    }

    // Sample batch from experience buffer
    const batch = this.sampleBatch(32);
    const similarExperiences = await this.retrieveSimilarExperiences(batch);

    // Combine local and similar experiences
    const trainingBatch = [...batch, ...similarExperiences];

    // Train networks
    const { policyLoss, valueLoss } = await this.trainNetworks(trainingBatch);

    // Decay exploration
    this.epsilon = Math.max(0.01, this.epsilon * 0.995);

    return {
      loss: policyLoss + valueLoss,
      policyLoss,
      valueLoss
    };
  }

  private sampleBatch(batchSize: number): Experience[] {
    const indices = Array.from({ length: Math.min(batchSize, this.experienceBuffer.length) },
      () => Math.floor(Math.random() * this.experienceBuffer.length));
    return indices.map(i => this.experienceBuffer[i]);
  }

  private async retrieveSimilarExperiences(batch: Experience[]): Promise<Experience[]> {
    const similarExperiences: Experience[] = [];

    for (const experience of batch) {
      const stateVector = Array.from((await experience.state.data()) as Float32Array);
      const embedding = await computeEmbedding(JSON.stringify(stateVector));

      // Retrieve similar experiences from AgentDB
      const result = await this.agentDB.retrieveWithReasoning(embedding, {
        domain: 'ran-reinforcement-learning',
        k: 5,
        useMMR: true
      });

      // Convert stored experiences back to Experience format
      for (const memory of result.memories) {
        const storedExp = memory.pattern;
        const exp: Experience = {
          state: tf.tensor2d([storedExp.stateVector]),
          action: storedExp.action,
          reward: storedExp.reward,
          nextState: tf.tensor2d([storedExp.nextStateVector]),
          done: storedExp.done,
          timestamp: storedExp.timestamp
        };
        similarExperiences.push(exp);
      }
    }

    return similarExperiences.slice(0, 16); // Limit to 16 additional experiences
  }

  private async trainNetworks(batch: Experience[]): Promise<{ policyLoss: number, valueLoss: number }> {
    // Prepare training data
    const states = tf.concat(batch.map(exp => exp.state));
    const actions = tf.tensor1d(batch.map(exp => exp.action), 'int32');
    const rewards = tf.tensor1d(batch.map(exp => exp.reward));
    const nextStates = tf.concat(batch.map(exp => exp.nextState));
    const dones = tf.tensor1d(batch.map(exp => exp.done ? 1 : 0));

    // Calculate advantages using value network
    const nextValues = this.valueNetwork.predict(nextStates) as tf.Tensor;
    const currentValues = this.valueNetwork.predict(states) as tf.Tensor;

    const targets = rewards.add(
      nextValues.mul(
        tf.scalar(0.95).mul(
          tf.scalar(1).sub(dones)
        )
      )
    );

    const advantages = targets.sub(currentValues);

    // Train value network
    const valueHistory = await this.valueNetwork.fit(states, targets, {
      epochs: 1,
      batchSize: batch.length,
      verbose: 0
    });

    // Train policy network
    const actionProbs = this.policyNetwork.predict(states) as tf.Tensor;
    const actionMask = tf.oneHot(actions, 8);
    const policyGradients = advantages.mul(
      tf.log(
        tf.sum(actionProbs.mul(actionMask), 1).add(1e-8)
      )
    ).neg();

    const policyHistory = await this.policyNetwork.fit(states, actionMask, {
      epochs: 1,
      batchSize: batch.length,
      sampleWeights: policyGradients.abs(),
      verbose: 0
    });

    // Cleanup tensors
    states.dispose();
    actions.dispose();
    rewards.dispose();
    nextStates.dispose();
    dones.dispose();
    nextValues.dispose();
    currentValues.dispose();
    targets.dispose();
    advantages.dispose();
    actionProbs.dispose();
    actionMask.dispose();
    policyGradients.dispose();

    return {
      policyLoss: (policyHistory.history.loss?.[0] || 0),
      valueLoss: (valueHistory.history.loss?.[0] || 0)
    };
  }

  async loadExperiencesFromAgentDB() {
    const embedding = await computeEmbedding('rl-experience');
    const result = await this.agentDB.retrieveWithReasoning(embedding, {
      domain: 'ran-reinforcement-learning',
      k: 1000,
      filters: {
        timestamp: { $gte: Date.now() - 7 * 24 * 3600000 } // Last 7 days
      }
    });

    console.log(`Loaded ${result.memories.length} experiences from AgentDB`);
  }

  getActionName(action: number): string {
    const actions = [
      'increase_power',
      'decrease_power',
      'adjust_beamforming',
      'optimize_handover',
      'activate_carrier',
      'deactivate_carrier',
      'adjust_antenna_tilt',
      'modify_scheduler'
    ];
    return actions[action] || 'unknown';
  }

  async evaluatePolicy(testStates: RANState[]): Promise<EvaluationMetrics> {
    let totalReward = 0;
    let totalSteps = 0;
    const evaluations: Array<{ state: RANState, action: number, reward: number }> = [];

    for (const state of testStates) {
      const action = await this.selectAction(state);
      const reward = await this.calculateReward(state, action);

      totalReward += reward;
      totalSteps++;
      evaluations.push({ state, action, reward });
    }

    return {
      averageReward: totalReward / totalSteps,
      totalStates: testStates.length,
      evaluations,
      explorationRate: this.epsilon
    };
  }

  private async calculateReward(state: RANState, action: number): Promise<number> {
    // Multi-objective reward function
    const actionName = this.getActionName(action);
    let reward = 0;

    // Energy efficiency objective (30% weight)
    const energyReward = this.calculateEnergyReward(state, actionName);
    reward += energyReward * 0.3;

    // Mobility objective (25% weight)
    const mobilityReward = this.calculateMobilityReward(state, actionName);
    reward += mobilityReward * 0.25;

    // Coverage objective (25% weight)
    const coverageReward = this.calculateCoverageReward(state, actionName);
    reward += coverageReward * 0.25;

    // Capacity objective (20% weight)
    const capacityReward = this.calculateCapacityReward(state, actionName);
    reward += capacityReward * 0.2;

    return reward;
  }

  private calculateEnergyReward(state: RANState, action: string): number {
    switch (action) {
      case 'decrease_power':
        return state.energyConsumption > 100 ? 0.5 : 0.1;
      case 'increase_power':
        return state.energyConsumption < 50 ? 0.3 : -0.2;
      case 'deactivate_carrier':
        return state.userCount < 20 ? 0.4 : -0.1;
      case 'activate_carrier':
        return state.userCount > 80 ? 0.3 : -0.2;
      default:
        return 0;
    }
  }

  private calculateMobilityReward(state: RANState, action: string): number {
    switch (action) {
      case 'optimize_handover':
        return state.mobilityIndex > 70 ? 0.4 : 0.1;
      case 'adjust_beamforming':
        return state.mobilityIndex > 50 ? 0.3 : 0.1;
      default:
        return 0;
    }
  }

  private calculateCoverageReward(state: RANState, action: string): number {
    switch (action) {
      case 'increase_power':
        return state.coverageHoleCount > 10 ? 0.4 : 0.1;
      case 'adjust_antenna_tilt':
        return state.coverageHoleCount > 5 ? 0.3 : 0.1;
      default:
        return 0;
    }
  }

  private calculateCapacityReward(state: RANState, action: string): number {
    switch (action) {
      case 'activate_carrier':
        return state.throughput < 500 ? 0.4 : 0.1;
      case 'modify_scheduler':
        return state.userCount > 60 ? 0.3 : 0.1;
      default:
        return 0;
    }
  }
}

interface RANState {
  throughput: number;
  latency: number;
  packetLoss: number;
  signalStrength: number;
  interference: number;
  energyConsumption: number;
  userCount: number;
  mobilityIndex: number;
  coverageHoleCount: number;
}

interface Experience {
  state: tf.Tensor;
  action: number;
  reward: number;
  nextState: tf.Tensor;
  done: boolean;
  timestamp: number;
}

interface TrainingMetrics {
  loss: number;
  policyLoss: number;
  valueLoss: number;
}

interface EvaluationMetrics {
  averageReward: number;
  totalStates: number;
  evaluations: Array<{ state: RANState, action: number, reward: number }>;
  explorationRate: number;
}

class RANEnvironment {
  private currentState: RANState;
  private agentDB: AgentDBAdapter;

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-rl.db',
      enableLearning: true,
      cacheSize: 2000,
    });

    this.currentState = this.generateInitialState();
  }

  private generateInitialState(): RANState {
    return {
      throughput: 400 + Math.random() * 400,
      latency: 20 + Math.random() * 60,
      packetLoss: Math.random() * 0.05,
      signalStrength: -60 - Math.random() * 40,
      interference: Math.random() * 0.2,
      energyConsumption: 50 + Math.random() * 100,
      userCount: 10 + Math.random() * 90,
      mobilityIndex: Math.random() * 100,
      coverageHoleCount: Math.floor(Math.random() * 20)
    };
  }

  async step(action: number): Promise<{ nextState: RANState, reward: number, done: boolean }> {
    const actionName = this.getActionName(action);

    // Apply action to get next state
    const nextState = await this.applyAction(this.currentState, actionName);

    // Calculate reward
    const reward = this.calculateReward(this.currentState, actionName, nextState);

    // Check if episode is done
    const done = this.checkEpisodeDone(nextState);

    // Update current state
    this.currentState = nextState;

    return { nextState, reward, done };
  }

  reset(): RANState {
    this.currentState = this.generateInitialState();
    return this.currentState;
  }

  private async applyAction(state: RANState, action: string): Promise<RANState> {
    const nextState = { ...state };

    switch (action) {
      case 'increase_power':
        nextState.signalStrength += 3 + Math.random() * 4;
        nextState.energyConsumption *= 1.1;
        nextState.interference *= 1.05;
        break;

      case 'decrease_power':
        nextState.signalStrength -= 2 + Math.random() * 3;
        nextState.energyConsumption *= 0.9;
        nextState.interference *= 0.95;
        break;

      case 'adjust_beamforming':
        nextState.signalStrength += 2 + Math.random() * 6;
        nextState.interference *= 0.9;
        nextState.coverageHoleCount = Math.max(0, nextState.coverageHoleCount - Math.floor(Math.random() * 3));
        break;

      case 'optimize_handover':
        if (nextState.mobilityIndex > 50) {
          nextState.latency *= 0.9;
          nextState.packetLoss *= 0.8;
          nextState.throughput *= 1.05;
        }
        break;

      case 'activate_carrier':
        if (nextState.userCount > 60) {
          nextState.throughput *= 1.3;
          nextState.energyConsumption *= 1.2;
          nextState.latency *= 0.85;
        }
        break;

      case 'deactivate_carrier':
        if (nextState.userCount < 30) {
          nextState.energyConsumption *= 0.7;
          nextState.throughput *= 0.6;
        }
        break;

      case 'adjust_antenna_tilt':
        nextState.coverageHoleCount = Math.max(0, nextState.coverageHoleCount - Math.floor(Math.random() * 5));
        nextState.signalStrength += (Math.random() - 0.5) * 4;
        break;

      case 'modify_scheduler':
        if (nextState.userCount > 40) {
          nextState.latency *= 0.9;
          nextState.throughput *= 1.1;
        }
        break;
    }

    // Add some natural variation
    nextState.throughput *= (1 + (Math.random() - 0.5) * 0.1);
    nextState.latency *= (1 + (Math.random() - 0.5) * 0.1);
    nextState.mobilityIndex += (Math.random() - 0.5) * 10;

    // Ensure values stay within reasonable bounds
    this.clampStateValues(nextState);

    return nextState;
  }

  private calculateReward(currentState: RANState, action: string, nextState: RANState): number {
    let reward = 0;

    // Energy efficiency reward
    const energyImprovement = (currentState.energyConsumption - nextState.energyConsumption) / currentState.energyConsumption;
    reward += energyImprovement * 0.3;

    // Throughput reward
    const throughputImprovement = (nextState.throughput - currentState.throughput) / currentState.throughput;
    reward += throughputImprovement * 0.25;

    // Latency reward (lower is better)
    const latencyImprovement = (currentState.latency - nextState.latency) / currentState.latency;
    reward += latencyImprovement * 0.2;

    // Signal quality reward
    const signalImprovement = (nextState.signalStrength - currentState.signalStrength) / 100;
    reward += signalImprovement * 0.15;

    // Coverage reward
    const coverageImprovement = (currentState.coverageHoleCount - nextState.coverageHoleCount) / Math.max(1, currentState.coverageHoleCount);
    reward += coverageImprovement * 0.1;

    // Penalty for extreme values
    if (nextState.packetLoss > 0.05) reward -= 0.2;
    if (nextState.latency > 100) reward -= 0.1;
    if (nextState.signalStrength < -110) reward -= 0.3;

    return Math.max(-1, Math.min(1, reward));
  }

  private checkEpisodeDone(state: RANState): boolean {
    // Episode ends if critical conditions are met
    return (
      state.signalStrength < -120 ||  // Critical signal loss
      state.packetLoss > 0.1 ||       // Critical packet loss
      state.latency > 200             // Critical latency
    );
  }

  private clampStateValues(state: RANState) {
    state.throughput = Math.max(50, Math.min(2000, state.throughput));
    state.latency = Math.max(5, Math.min(200, state.latency));
    state.packetLoss = Math.max(0, Math.min(0.2, state.packetLoss));
    state.signalStrength = Math.max(-120, Math.min(-50, state.signalStrength));
    state.interference = Math.max(0, Math.min(0.5, state.interference));
    state.energyConsumption = Math.max(20, Math.min(300, state.energyConsumption));
    state.userCount = Math.max(1, Math.min(200, state.userCount));
    state.mobilityIndex = Math.max(0, Math.min(100, state.mobilityIndex));
    state.coverageHoleCount = Math.max(0, Math.min(50, state.coverageHoleCount));
  }

  getActionName(action: number): string {
    const actions = [
      'increase_power',
      'decrease_power',
      'adjust_beamforming',
      'optimize_handover',
      'activate_carrier',
      'deactivate_carrier',
      'adjust_antenna_tilt',
      'modify_scheduler'
    ];
    return actions[action] || 'unknown';
  }

  getCurrentState(): RANState {
    return this.currentState;
  }
}

import * as tf from '@tensorflow/tfjs-node';

class RANMultiObjectivePPO {
  private policyNetwork: tf.LayersModel;
  private valueNetwork: tf.LayersModel;
  private agentDB: AgentDBAdapter;
  private objectives: MultiObjectiveConfig;
  private experienceBuffer: PPOExperience[];

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-rl.db',
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 3000,
    });

    this.objectives = {
      energy: { weight: 0.3, target: 'minimize' },
      throughput: { weight: 0.25, target: 'maximize' },
      latency: { weight: 0.2, target: 'minimize' },
      coverage: { weight: 0.15, target: 'maximize' },
      mobility: { weight: 0.1, target: 'maximize' }
    };

    await this.buildNetworks();
    this.experienceBuffer = [];
    await this.loadPPOExperiences();
  }

  private async buildNetworks() {
    // Advanced policy network with attention mechanism
    const stateInput = tf.input({ shape: [12] });

    // Feature extraction layers
    const dense1 = tf.layers.dense({ units: 256, activation: 'relu' }).apply(stateInput) as tf.SymbolicTensor;
    const dropout1 = tf.layers.dropout({ rate: 0.2 }).apply(dense1) as tf.SymbolicTensor;

    // Attention mechanism for multi-objective focus
    const attention = tf.layers.multiHeadAttention({
      numHeads: 8,
      keyDim: 32
    }).apply([dropout1, dropout1]) as tf.SymbolicTensor;

    const dense2 = tf.layers.dense({ units: 128, activation: 'relu' }).apply(attention) as tf.SymbolicTensor;
    const dropout2 = tf.layers.dropout({ rate: 0.2 }).apply(dense2) as tf.SymbolicTensor;

    // Objective-specific heads
    const energyHead = tf.layers.dense({ units: 64, activation: 'relu', name: 'energy_head' }).apply(dropout2) as tf.SymbolicTensor;
    const throughputHead = tf.layers.dense({ units: 64, activation: 'relu', name: 'throughput_head' }).apply(dropout2) as tf.SymbolicTensor;
    const latencyHead = tf.layers.dense({ units: 64, activation: 'relu', name: 'latency_head' }).apply(dropout2) as tf.SymbolicTensor;

    // Combine objective features
    const combined = tf.layers.concatenate().apply([energyHead, throughputHead, latencyHead]) as tf.SymbolicTensor;
    const dense3 = tf.layers.dense({ units: 64, activation: 'relu' }).apply(combined) as tf.SymbolicTensor;

    // Output layer
    const actionOutput = tf.layers.dense({ units: 8, activation: 'softmax', name: 'action_output' }).apply(dense3) as tf.SymbolicTensor;

    this.policyNetwork = tf.model({ inputs: stateInput, outputs: actionOutput });

    // Value network with shared features
    const valueDense1 = tf.layers.dense({ units: 256, activation: 'relu' }).apply(stateInput) as tf.SymbolicTensor;
    const valueDense2 = tf.layers.dense({ units: 128, activation: 'relu' }).apply(valueDense1) as tf.SymbolicTensor;
    const valueOutput = tf.layers.dense({ units: 1, activation: 'linear', name: 'value_output' }).apply(valueDense2) as tf.SymbolicTensor;

    this.valueNetwork = tf.model({ inputs: stateInput, outputs: valueOutput });

    // Compile networks
    this.policyNetwork.compile({
      optimizer: tf.train.adam(0.0003),
      loss: 'categoricalCrossentropy'
    });

    this.valueNetwork.compile({
      optimizer: tf.train.adam(0.001),
      loss: 'meanSquaredError'
    });
  }

  async selectAction(state: RANState, deterministic: boolean = false): Promise<{ action: number, logProb: number, value: number }> {
    const stateTensor = this.encodeState(state);

    // Get action probabilities
    const actionProbs = this.policyNetwork.predict(stateTensor) as tf.Tensor;
    const probsArray = await actionProbs.data();

    // Get value estimate
    const valueTensor = this.valueNetwork.predict(stateTensor) as tf.Tensor;
    const valueArray = await valueTensor.data();

    let action: number;
    let logProb: number;

    if (deterministic) {
      // Select most probable action
      action = tf.argMax(actionProbs, 1).dataSync()[0];
      logProb = Math.log(probsArray[action] + 1e-8);
    } else {
      // Sample from distribution
      const randomValue = Math.random();
      let cumulativeProb = 0;

      for (let i = 0; i < probsArray.length; i++) {
        cumulativeProb += probsArray[i];
        if (randomValue < cumulativeProb) {
          action = i;
          logProb = Math.log(probsArray[i] + 1e-8);
          break;
        }
      }

      if (action === undefined) {
        action = probsArray.length - 1;
        logProb = Math.log(probsArray[action] + 1e-8);
      }
    }

    stateTensor.dispose();
    actionProbs.dispose();
    valueTensor.dispose();

    return { action, logProb, value: valueArray[0] };
  }

  async storeExperience(
    state: RANState,
    action: number,
    reward: MultiObjectiveReward,
    nextState: RANState,
    logProb: number,
    value: number,
    done: boolean
  ) {
    const experience: PPOExperience = {
      state: this.encodeState(state),
      action,
      reward,
      nextState: this.encodeState(nextState),
      logProb,
      value,
      done,
      timestamp: Date.now()
    };

    this.experienceBuffer.push(experience);

    // Store in AgentDB with multi-objective tagging
    await this.storePPOExperienceInAgentDB(experience);

    // Maintain buffer size
    if (this.experienceBuffer.length > 5000) {
      this.experienceBuffer.shift();
    }
  }

  private async storePPOExperienceInAgentDB(experience: PPOExperience) {
    const experienceData = {
      stateVector: Array.from((await experience.state.data()) as Float32Array),
      action: experience.action,
      reward: experience.reward,
      nextStateVector: Array.from((await experience.nextState.data()) as Float32Array),
      logProb: experience.logProb,
      value: experience.value,
      done: experience.done,
      timestamp: experience.timestamp,
      objectives: Object.keys(experience.reward),
      dominantObjective: this.getDominantObjective(experience.reward)
    };

    const embedding = await computeEmbedding(JSON.stringify(experienceData));

    await this.agentDB.insertPattern({
      id: '',
      type: 'ppo-experience',
      domain: 'ran-multi-objective-rl',
      pattern_data: JSON.stringify({ embedding, pattern: experienceData }),
      confidence: this.calculateExperienceConfidence(experience),
      usage_count: 1,
      success_count: this.calculateExperienceSuccess(experience),
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  private getDominantObjective(reward: MultiObjectiveReward): string {
    let maxReward = -Infinity;
    let dominantObjective = '';

    for (const [objective, value] of Object.entries(reward)) {
      const weightedValue = value * this.objectives[objective].weight;
      if (weightedValue > maxReward) {
        maxReward = weightedValue;
        dominantObjective = objective;
      }
    }

    return dominantObjective;
  }

  private calculateExperienceConfidence(experience: PPOExperience): number {
    // Confidence based on reward magnitude and consistency
    const totalReward = Object.values(experience.reward).reduce((sum, val) => sum + Math.abs(val), 0);
    return Math.min(totalReward / 2, 1.0);
  }

  private calculateExperienceSuccess(experience: PPOExperience): number {
    // Success based on positive dominant objective
    const dominant = this.getDominantObjective(experience.reward);
    const dominantValue = experience.reward[dominant];
    const target = this.objectives[dominant].target;

    if (target === 'maximize') {
      return dominantValue > 0 ? 1 : 0;
    } else {
      return dominantValue < 0 ? 1 : 0;
    }
  }

  async trainPPOEpoch(epochs: number = 10, batchSize: number = 64): Promise<PPOTrainingMetrics> {
    if (this.experienceBuffer.length < batchSize) {
      return { totalLoss: 0, policyLoss: 0, valueLoss: 0, entropyLoss: 0, klDivergence: 0 };
    }

    let totalPolicyLoss = 0;
    let totalValueLoss = 0;
    let totalEntropyLoss = 0;
    let totalKLDivergence = 0;

    // Compute advantages for all experiences
    const advantages = await this.computeAdvantages();

    for (let epoch = 0; epoch < epochs; epoch++) {
      // Shuffle experiences
      const shuffled = this.shuffleArray([...this.experienceBuffer]);

      for (let i = 0; i < shuffled.length; i += batchSize) {
        const batch = shuffled.slice(i, i + batchSize);
        const batchAdvantages = advantages.slice(i, i + batchSize);

        const metrics = await this.trainPPOBatch(batch, batchAdvantages);

        totalPolicyLoss += metrics.policyLoss;
        totalValueLoss += metrics.valueLoss;
        totalEntropyLoss += metrics.entropyLoss;
        totalKLDivergence += metrics.klDivergence;
      }
    }

    const numBatches = (epochs * Math.ceil(this.experienceBuffer.length / batchSize));

    return {
      totalLoss: (totalPolicyLoss + totalValueLoss + totalEntropyLoss) / numBatches,
      policyLoss: totalPolicyLoss / numBatches,
      valueLoss: totalValueLoss / numBatches,
      entropyLoss: totalEntropyLoss / numBatches,
      klDivergence: totalKLDivergence / numBatches
    };
  }

  private async computeAdvantages(): Promise<number[]> {
    const advantages: number[] = [];
    let nextValue = 0;
    const gamma = 0.99;
    const lambda = 0.95;

    // Compute returns and advantages backwards
    for (let i = this.experienceBuffer.length - 1; i >= 0; i--) {
      const experience = this.experienceBuffer[i];

      // Multi-objective reward aggregation
      const totalReward = this.aggregateRewards(experience.reward);

      const delta = totalReward + (experience.done ? 0 : gamma * nextValue) - experience.value;

      // GAE (Generalized Advantage Estimation)
      const advantage = delta + (experience.done ? 0 : gamma * lambda * (advantages[0] || 0));

      advantages.unshift(advantage);
      nextValue = experience.value;
    }

    // Normalize advantages
    const mean = advantages.reduce((sum, adv) => sum + adv, 0) / advantages.length;
    const std = Math.sqrt(advantages.reduce((sum, adv) => sum + Math.pow(adv - mean, 2), 0) / advantages.length);

    return advantages.map(adv => (adv - mean) / (std + 1e-8));
  }

  private aggregateRewards(reward: MultiObjectiveReward): number {
    let total = 0;

    for (const [objective, value] of Object.entries(reward)) {
      const weight = this.objectives[objective].weight;
      const target = this.objectives[objective].target;

      // Apply sign based on optimization target
      const signedValue = target === 'maximize' ? value : -value;
      total += signedValue * weight;
    }

    return total;
  }

  private async trainPPOBatch(batch: PPOExperience[], advantages: number[]): Promise<PPOBatchMetrics> {
    // Prepare batch tensors
    const states = tf.concat(batch.map(exp => exp.state));
    const actions = tf.tensor1d(batch.map(exp => exp.action), 'int32');
    const oldLogProbs = tf.tensor1d(batch.map(exp => exp.logProb));
    const oldValues = tf.tensor1d(batch.map(exp => exp.value));
    const advantagesTensor = tf.tensor1d(advantages);
    const returns = tf.tensor1d(batch.map((exp, i) => {
      const totalReward = this.aggregateRewards(exp.reward);
      return totalReward + (exp.done ? 0 : 0.99 * oldValues.dataSync()[i]);
    }));

    // Get current policy and value predictions
    const currentActionProbs = this.policyNetwork.predict(states) as tf.Tensor;
    const currentValues = this.valueNetwork.predict(states) as tf.Tensor;

    // Calculate new log probabilities
    const actionMask = tf.oneHot(actions, 8);
    const newLogProbs = tf.sum(
      tf.log(
        tf.sum(currentActionProbs.mul(actionMask), 1).add(1e-8)
      ),
      1
    );

    // PPO policy loss
    const ratio = tf.exp(newLogProbs.sub(oldLogProbs));
    const surr1 = ratio.mul(advantagesTensor);
    const surr2 = tf.clamp(ratio, 0.8, 1.2).mul(advantagesTensor);
    const policyLoss = tf.neg(tf.minimum(surr1, surr2).mean());

    // Value function loss
    const valueLoss = tf.losses.meanSquaredError(returns, currentValues);

    // Entropy bonus for exploration
    const entropy = tf.neg(
      tf.sum(
        currentActionProbs.mul(tf.log(currentActionProbs.add(1e-8))),
        1
      ).mean()
    );

    // KL divergence for early stopping
    const klDivergence = tf.mean(newLogProbs.sub(oldLogProbs));

    // Combined loss
    const totalLoss = policyLoss.add(valueLoss.mul(0.5)).sub(entropy.mul(0.01));

    // Apply gradients
    const grads = tf.variableGrads(() => totalLoss);
    const optimizer = tf.train.adam(0.0001);
    optimizer.applyGradients(grads.grads);

    // Get loss values
    const lossValues = await Promise.all([
      policyLoss.data(),
      valueLoss.data(),
      entropy.data(),
      klDivergence.data()
    ]);

    // Cleanup tensors
    states.dispose();
    actions.dispose();
    oldLogProbs.dispose();
    oldValues.dispose();
    advantagesTensor.dispose();
    returns.dispose();
    currentActionProbs.dispose();
    currentValues.dispose();
    actionMask.dispose();
    newLogProbs.dispose();
    ratio.dispose();
    surr1.dispose();
    surr2.dispose();
    policyLoss.dispose();
    valueLoss.dispose();
    entropy.dispose();
    klDivergence.dispose();
    totalLoss.dispose();
    Object.values(grads.grads).forEach(grad => grad.dispose());

    return {
      policyLoss: lossValues[0][0],
      valueLoss: lossValues[1][0],
      entropyLoss: lossValues[2][0],
      klDivergence: lossValues[3][0]
    };
  }

  private encodeState(state: RANState): tf.Tensor {
    // Enhanced state encoding with multi-objective features
    const encoded = [
      state.throughput / 1000,        // Normalized throughput
      state.latency / 100,           // Normalized latency
      state.packetLoss,               // Packet loss
      state.signalStrength / 100,     // Normalized signal strength
      state.interference,             // Interference
      state.energyConsumption / 200,  // Normalized energy consumption
      state.userCount / 100,         // Normalized user count
      state.mobilityIndex / 100,     // Normalized mobility index
      state.coverageHoleCount / 50,  // Normalized coverage holes

      // Additional features for multi-objective optimization
      this.calculateEnergyEfficiency(state),
      this.calculateCoverageQuality(state),
      this.calculateMobilityComplexity(state),
      this.calculateLoadBalance(state)
    ];

    return tf.tensor2d([encoded]);
  }

  private calculateEnergyEfficiency(state: RANState): number {
    // Energy efficiency: throughput per watt
    return Math.min(state.throughput / state.energyConsumption / 10, 1.0);
  }

  private calculateCoverageQuality(state: RANState): number {
    // Coverage quality based on signal and holes
    const signalQuality = Math.max(0, (state.signalStrength + 50) / 50);
    const holePenalty = Math.max(0, 1 - state.coverageHoleCount / 20);
    return signalQuality * holePenalty;
  }

  private calculateMobilityComplexity(state: RANState): number {
    // Mobility complexity based on user movement and handover needs
    return Math.min(state.mobilityIndex / 100, 1.0);
  }

  private calculateLoadBalance(state: RANState): number {
    // Load balance: optimal user count range
    const optimalUsers = 50;
    const deviation = Math.abs(state.userCount - optimalUsers) / optimalUsers;
    return Math.max(0, 1 - deviation);
  }

  private shuffleArray<T>(array: T[]): T[] {
    const shuffled = [...array];
    for (let i = shuffled.length - 1; i > 0; i--) {
      const j = Math.floor(Math.random() * (i + 1));
      [shuffled[i], shuffled[j]] = [shuffled[j], shuffled[i]];
    }
    return shuffled;
  }

  private async loadPPOExperiences() {
    const embedding = await computeEmbedding('ppo-experience');
    const result = await this.agentDB.retrieveWithReasoning(embedding, {
      domain: 'ran-multi-objective-rl',
      k: 2000,
      filters: {
        timestamp: { $gte: Date.now() - 14 * 24 * 3600000 } // Last 14 days
      }
    });

    console.log(`Loaded ${result.memories.length} PPO experiences from AgentDB`);
  }

  async evaluateMultiObjectivePolicy(testStates: RANState[]): Promise<MultiObjectiveEvaluation> {
    const evaluations: Array<{
      state: RANState,
      action: number,
      rewards: MultiObjectiveReward,
      totalReward: number
    }> = [];

    let totalRewards: MultiObjectiveReward = {
      energy: 0,
      throughput: 0,
      latency: 0,
      coverage: 0,
      mobility: 0
    };

    for (const state of testStates) {
      const { action } = await this.selectAction(state, true);
      const rewards = await this.calculateMultiObjectiveReward(state, action);
      const totalReward = this.aggregateRewards(rewards);

      evaluations.push({ state, action, rewards, totalReward });

      // Accumulate rewards
      for (const [objective, reward] of Object.entries(rewards)) {
        totalRewards[objective] += reward;
      }
    }

    // Calculate averages
    const numStates = testStates.length;
    const avgRewards: MultiObjectiveReward = {} as MultiObjectiveReward;
    for (const [objective, total] of Object.entries(totalRewards)) {
      avgRewards[objective] = total / numStates;
    }

    return {
      averageRewards: avgRewards,
      averageTotalReward: evaluations.reduce((sum, eval) => sum + eval.totalReward, 0) / numStates,
      evaluations,
      objectivePerformance: this.calculateObjectivePerformance(avgRewards)
    };
  }

  private async calculateMultiObjectiveReward(state: RANState, action: number): Promise<MultiObjectiveReward> {
    const actionName = this.getActionName(action);
    const nextState = await this.simulateAction(state, actionName);

    return {
      energy: this.calculateEnergyReward(state, nextState, actionName),
      throughput: this.calculateThroughputReward(state, nextState, actionName),
      latency: this.calculateLatencyReward(state, nextState, actionName),
      coverage: this.calculateCoverageReward(state, nextState, actionName),
      mobility: this.calculateMobilityReward(state, nextState, actionName)
    };
  }

  private async simulateAction(state: RANState, action: string): Promise<RANState> {
    // Simulate action effect (simplified version of environment simulation)
    const nextState = { ...state };

    switch (action) {
      case 'increase_power':
        nextState.signalStrength += 3 + Math.random() * 4;
        nextState.energyConsumption *= 1.1;
        break;
      case 'decrease_power':
        nextState.signalStrength -= 2 + Math.random() * 3;
        nextState.energyConsumption *= 0.9;
        break;
      // ... other actions
    }

    this.clampStateValues(nextState);
    return nextState;
  }

  private clampStateValues(state: RANState) {
    state.throughput = Math.max(50, Math.min(2000, state.throughput));
    state.latency = Math.max(5, Math.min(200, state.latency));
    state.signalStrength = Math.max(-120, Math.min(-50, state.signalStrength));
    state.energyConsumption = Math.max(20, Math.min(300, state.energyConsumption));
    // ... clamp other values
  }

  private calculateEnergyReward(currentState: RANState, nextState: RANState, action: string): number {
    const energyChange = (currentState.energyConsumption - nextState.energyConsumption) / currentState.energyConsumption;

    // Consider action-specific energy impacts
    let actionBonus = 0;
    if (action === 'decrease_power' && nextState.energyConsumption < 80) actionBonus = 0.2;
    if (action === 'deactivate_carrier' && nextState.userCount < 30) actionBonus = 0.3;

    return energyChange + actionBonus;
  }

  private calculateThroughputReward(currentState: RANState, nextState: RANState, action: string): number {
    const throughputChange = (nextState.throughput - currentState.throughput) / currentState.throughput;

    // Action-specific throughput bonuses
    let actionBonus = 0;
    if (action === 'activate_carrier' && nextState.userCount > 80) actionBonus = 0.3;
    if (action === 'modify_scheduler' && nextState.userCount > 60) actionBonus = 0.2;

    return throughputChange + actionBonus;
  }

  private calculateLatencyReward(currentState: RANState, nextState: RANState, action: string): number {
    const latencyChange = (currentState.latency - nextState.latency) / currentState.latency;

    // Action-specific latency bonuses
    let actionBonus = 0;
    if (action === 'optimize_handover') actionBonus = 0.2;
    if (action === 'modify_scheduler') actionBonus = 0.1;

    return latencyChange + actionBonus;
  }

  private calculateCoverageReward(currentState: RANState, nextState: RANState, action: string): number {
    const coverageChange = (currentState.coverageHoleCount - nextState.coverageHoleCount) / Math.max(1, currentState.coverageHoleCount);

    // Action-specific coverage bonuses
    let actionBonus = 0;
    if (action === 'increase_power' && nextState.coverageHoleCount < 5) actionBonus = 0.2;
    if (action === 'adjust_antenna_tilt') actionBonus = 0.15;

    return coverageChange + actionBonus;
  }

  private calculateMobilityReward(currentState: RANState, nextState: RANState, action: string): number {
    const mobilityChange = (nextState.mobilityIndex - currentState.mobilityIndex) / 100;

    // Action-specific mobility bonuses
    let actionBonus = 0;
    if (action === 'optimize_handover' && nextState.mobilityIndex > 70) actionBonus = 0.3;
    if (action === 'adjust_beamforming') actionBonus = 0.2;

    return mobilityChange + actionBonus;
  }

  private calculateObjectivePerformance(avgRewards: MultiObjectiveReward): { [objective: string]: { score: number, status: string } } {
    const performance: { [objective: string]: { score: number, status: string } } = {};

    for (const [objective, avgReward] of Object.entries(avgRewards)) {
      const target = this.objectives[objective].target;
      const weight = this.objectives[objective].weight;

      let score: number;
      let status: string;

      if (target === 'maximize') {
        score = Math.max(0, avgReward);
        status = score > 0.1 ? 'excellent' : score > 0.05 ? 'good' : score > 0 ? 'fair' : 'poor';
      } else {
        score = Math.max(0, -avgReward);
        status = score > 0.1 ? 'excellent' : score > 0.05 ? 'good' : score > 0 ? 'fair' : 'poor';
      }

      performance[objective] = { score, status };
    }

    return performance;
  }

  getActionName(action: number): string {
    const actions = [
      'increase_power',
      'decrease_power',
      'adjust_beamforming',
      'optimize_handover',
      'activate_carrier',
      'deactivate_carrier',
      'adjust_antenna_tilt',
      'modify_scheduler'
    ];
    return actions[action] || 'unknown';
  }
}

interface MultiObjectiveConfig {
  [objective: string]: { weight: number, target: 'maximize' | 'minimize' };
}

interface MultiObjectiveReward {
  energy: number;
  throughput: number;
  latency: number;
  coverage: number;
  mobility: number;
}

interface PPOExperience {
  state: tf.Tensor;
  action: number;
  reward: MultiObjectiveReward;
  nextState: tf.Tensor;
  logProb: number;
  value: number;
  done: boolean;
  timestamp: number;
}

interface PPOTrainingMetrics {
  totalLoss: number;
  policyLoss: number;
  valueLoss: number;
  entropyLoss: number;
  klDivergence: number;
}

interface PPOBatchMetrics {
  policyLoss: number;
  valueLoss: number;
  entropyLoss: number;
  klDivergence: number;
}

interface MultiObjectiveEvaluation {
  averageRewards: MultiObjectiveReward;
  averageTotalReward: number;
  evaluations: Array<{
    state: RANState,
    action: number,
    rewards: MultiObjectiveReward,
    totalReward: number
  }>;
  objectivePerformance: { [objective: string]: { score: number, status: string } };
}

class RANHierarchicalRL {
  private highLevelPolicy: tf.LayersModel;  // Meta-controller
  private lowLevelPolicies: Map<string, tf.LayersModel>;  // Controllers
  private agentDB: AgentDBAdapter;
  private skillHierarchy: SkillHierarchy;
  private currentSkill: string;

  async initialize() {
    this.agentDB = await createAgentDBAdapter({
      dbPath: '.agentdb/ran-rl.db',
      enableLearning: true,
      enableReasoning: true,
      cacheSize: 3500,
    });

    this.skillHierarchy = {
      energy_optimization: {
        subSkills: ['reduce_power', 'deactivate_carrier', 'adjust_scheduler'],
        timeout: 30000,
        success_criteria: { energy_reduction: 0.1 }
      },
      capacity_optimization: {
        subSkills: ['activate_carrier', 'increase_power', 'modify_scheduler'],
        timeout: 20000,
        success_criteria: { throughput_increase: 0.15 }
      },
      mobility_optimization: {
        subSkills: ['optimize_handover', 'adjust_beamforming', 'modify_antenna'],
        timeout: 15000,
        success_criteria: { mobility_improvement: 0.2 }
      },
      coverage_optimization: {
        subSkills: ['increase_power', 'adjust_beamforming', 'modify_antenna'],
        timeout: 25000,
        success_criteria: { coverage_improvement: 0.1 }
      }
    };

    await this.buildHierarchicalNetworks();
    this.lowLevelPolicies = new Map();
    this.currentSkill = '';
  }

  private async buildHierarchicalNetworks() {
    // High-level policy (meta-controller)
    this.highLevelPolicy = tf.sequential({
      layers: [
        tf.layers.dense({ inputShape: [12], units: 128, activation: 'relu' }),
        tf.layers.dense({ units: 64, activation: 'relu' }),
        tf.layers.dense({ units: 32, activation: 'relu' }),
        tf.layers.dense({ units: 4, activation: 'softmax' }) // 4 high-level skills
      ]
    });

    this.highLevelPolicy.compile({
      optimizer: tf.train.adam(0.0001),
      loss: 'categoricalCrossentropy'
    });

    // Build low-level policies for each skill
    for (const skillName of Object.keys(this.skillHierarchy)) {
      const lowLevelPolicy = tf.sequential({
        layers: [
          tf.layers.dense({ inputShape: [14], units: 64, activation: 'relu' }), // 12 + 2 for skill context
          tf.layers.dense({ units: 32, activation: 'relu' }),
          tf.layers.dense({
            units: this.skillHierarchy[skillName].subSkills.length,
            activation: 'softmax'
          })
        ]
      });

      lowLevelPolicy.compile({
        optimizer: tf.train.adam(0.001),
        loss: 'categoricalCrossentropy'
      });

      this.lowLevelPolicies.set(skillName, lowLevelPolicy);
    }
  }

  async selectHighLevelSkill(state: RANState): Promise<{ skill: string, confidence: number }> {
    const stateTensor = this.encodeHighLevelState(state);
    const skillProbs = this.highLevelPolicy.predict(stateTensor) as tf.Tensor;
    const probsArray = await skillProbs.data();
    const skills = Object.keys(this.skillHierarchy);

    // Select skill with highest probability
    const maxIndex = tf.argMax(skillProbs, 1).dataSync()[0];
    const selectedSkill = skills[maxIndex];
    const confidence = probsArray[maxIndex];

    stateTensor.dispose();
    skillProbs.dispose();

    return { skill: selectedSkill, confidence };
  }

  async selectLowLevelAction(state: RANState, skill: string): Promise<{ action: number, confidence: number }> {
    const policy = this.lowLevelPolicies.get(skill);
    if (!policy) {
      throw new Error(`No low-level policy found for skill: ${skill}`);
    }

    const stateTensor = this.encodeLowLevelState(state, skill);
    const actionProbs = policy.predict(stateTensor) as tf.Tensor;
    const probsArray = await actionProbs.data();

    const maxIndex = tf.argMax(actionProbs, 1).dataSync()[0];
    const confidence = probsArray[maxIndex];

    stateTensor.dispose();
    actionProbs.dispose();

    return { action: maxIndex, confidence };
  }

  async executeHierarchicalPolicy(state: RANState): Promise<HierarchicalExecution> {
    const startTime = Date.now();

    // Step 1: Select high-level skill
    const { skill, confidence: skillConfidence } = await this.selectHighLevelSkill(state);
    this.currentSkill = skill;

    // Step 2: Execute low-level actions within skill context
    const skillExecution = await this.executeSkill(state, skill);

    // Step 3: Evaluate skill execution
    const evaluation = await this.evaluateSkillExecution(state, skillExecution);

    const executionTime = Date.now() - startTime;

    return {
      selectedSkill: skill,
      skillConfidence,
      lowLevelActions: skillExecution.actions,
      skillEvaluation: evaluation,
      executionTime,
      totalReward: evaluation.totalReward,
      success: evaluation.success
    };
  }

  private async executeSkill(state: RANState, skill: string): Promise<SkillExecution> {
    const skillConfig = this.skillHierarchy[skill];
    const actions: Array<{ action: string, confidence: number, timestamp: number }> = [];
    let currentState = state;
    let totalReward = 0;
    let steps = 0;
    const maxSteps = 10; // Limit steps per skill

    while (steps < maxSteps && steps < skillConfig.subSkills.length) {
      // Select low-level action
      const { action: actionIndex, confidence } = await this.selectLowLevelAction(currentState, skill);
      const actionName = skillConfig.subSkills[actionIndex];

      // Execute action
      const actionResult = await this.executeLowLevelAction(currentState, actionName);

      actions.push({
        action: actionName,
        confidence,
        timestamp: Date.now()
      });

      totalReward += actionResult.reward;
      currentState = actionResult.nextState;
      steps++;

      // Check if skill should terminate early
      if (await this.shouldTerminateSkill(currentState, skill, actionResult.reward)) {
        break;
      }
    }

    return {
      actions,
      totalReward,
      steps,
      finalState: currentState
    };
  }

  private async executeLowLevelAction(state: RANState, action: string): Promise<ActionResult> {
    const nextState = await this.simulateLowLevelAction(state, action);
    const reward = this.calculateLowLevelReward(state, nextState, action);
    const done = this.checkLowLevelTermination(nextState, action);

    return {
      nextState,
      reward,
      done,
      action
    };
  }

  private async simulateLowLevelAction(state: RANState, action: string): Promise<RANState> {
    const nextState = { ...state };

    switch (action) {
      case 'reduce_power':
        nextState.signalStrength -= 2 + Math.random() * 3;
        nextState.energyConsumption *= 0.85;
        break;

      case 'activate_carrier':
        if (state.userCount > 60) {
          nextState.throughput *= 1.4;
          nextState.energyConsumption *= 1.15;
          nextState.latency *= 0.85;
        }
        break;

      case 'optimize_handover':
        nextState.latency *= 0.9;
        nextState.packetLoss *= 0.8;
        nextState.mobilityIndex *= 1.1;
        break;

      case 'adjust_beamforming':
        nextState.signalStrength += 3 + Math.random() * 4;
        nextState.interference *= 0.85;
        nextState.coverageHoleCount = Math.max(0, nextState.coverageHoleCount - 2);
        break;

      // ... other low-level actions
    }

    // Add natural variation
    nextState.throughput *= (1 + (Math.random() - 0.5) * 0.05);
    nextState.mobilityIndex += (Math.random() - 0.5) * 5;

    this.clampStateValues(nextState);
    return nextState;
  }

  private calculateLowLevelReward(state: RANState, nextState: RANState, action: string): number {
    let reward = 0;

    // Basic performance improvements
    const throughputImprovement = (nextState.throughput - state.throughput) / state.throughput;
    const latencyImprovement = (state.latency - nextState.latency) / state.latency;
    const energyImprovement = (state.energyConsumption - nextState.energyConsumption) / state.energyConsumption;

    reward += throughputImprovement * 0.3 + latencyImprovement * 0.2 + energyImprovement * 0.3;

    // Action-specific rewards
    switch (action) {
      case 'reduce_power':
        reward += (state.energyConsumption - nextState.energyConsumption) > 10 ? 0.2 : 0;
        break;
      case 'optimize_handover':
        reward += nextState.mobilityIndex > state.mobilityIndex + 5 ? 0.15 : 0;
        break;
      case 'adjust_beamforming':
        reward += (nextState.signalStrength - state.signalStrength) > 2 ? 0.15 : 0;
        break;
    }

    // Penalties for negative impacts
    if (nextState.packetLoss > 0.05) reward -= 0.2;
    if (nextState.latency > 100) reward -= 0.1;

    return Math.max(-1, Math.min(1, reward));
  }

  private checkLowLevelTermination(state: RANState, action: string): boolean {
    // Terminate if critical conditions are met
    return (
      state.signalStrength < -115 ||
      state.packetLoss > 0.08 ||
      state.latency > 150
    );
  }

  private async shouldTerminateSkill(state: RANState, skill: string, recentReward: number): Promise<boolean> {
    const skillConfig = this.skillHierarchy[skill];

    // Check if success criteria are met
    for (const [criteria, threshold] of Object.entries(skillConfig.success_criteria)) {
      if (await this.checkSuccessCriteria(state, criteria, threshold)) {
        return true;
      }
    }

    // Check if recent performance is poor
    if (recentReward < -0.2) {
      return true;
    }

    return false;
  }

  private async checkSuccessCriteria(state: RANState, criteria: string, threshold: number): Promise<boolean> {
    switch (criteria) {
      case 'energy_reduction':
        // Would need initial state to compare
        return false; // Simplified
      case 'throughput_increase':
        // Would need initial state to compare
        return false; // Simplified
      case 'mobility_improvement':
        return state.mobilityIndex > 70;
      case 'coverage_improvement':
        return state.coverageHoleCount < 5;
      default:
        return false;
    }
  }

  private async evaluateSkillExecution(initialState: RANState, execution: SkillExecution): Promise<SkillEvaluation> {
    const skillConfig = this.skillHierarchy[this.currentSkill];
    const finalState = execution.finalState;

    // Check success criteria
    let successCriteriaMet = 0;
    const totalCriteria = Object.keys(skillConfig.success_criteria).length;

    for (const [criteria, threshold] of Object.entries(skillConfig.success_criteria)) {
      if (await this.checkSuccessCriteria(finalState, criteria, threshold)) {
        successCriteriaMet++;
      }
    }

    const success = successCriteriaMet >= Math.ceil(totalCriteria / 2);
    const successRate = successCriteriaMet / totalCriteria;

    // Calculate detailed rewards by objective
    const rewards = {
      overall: execution.totalReward / execution.steps,
      energy: this.calculateEnergyReward(initialState, finalState),
      throughput: this.calculateThroughputReward(initialState, finalState),
      latency: this.calculateLatencyReward(initialState, finalState),
      coverage: this.calculateCoverageReward(initialState, finalState)
    };

    return {
      success,
      successRate,
      totalReward: execution.totalReward,
      averageReward: execution.totalReward / execution.steps,
      rewards,
      steps: execution.steps,
      efficiency: execution.steps / skillConfig.subSkills.length,
      criteriaMet: successCriteriaMet
    };
  }

  private encodeHighLevelState(state: RANState): tf.Tensor {
    // Encode state for high-level skill selection
    const encoded = [
      state.throughput / 1000,
      state.latency / 100,
      state.signalStrength / 100,
      state.energyConsumption / 200,
      state.userCount / 100,
      state.mobilityIndex / 100,
      state.coverageHoleCount / 50,
      this.detectProblemType(state), // What problem needs solving?
      this.getUrgencyLevel(state), // How urgent is optimization?
      this.getResourcePressure(state), // Resource pressure level
      this.getTrafficPattern(state), // Traffic pattern type
      this.getEnvironmentalFactors(state) // Environmental factors
    ];

    return tf.tensor2d([encoded]);
  }

  private encodeLowLevelState(state: RANState, skill: string): tf.Tensor {
    // Encode state for low-level action selection
    const baseEncoding = [
      state.throughput / 1000,
      state.latency / 100,
      state.signalStrength / 100,
      state.energyConsumption / 200,
      state.userCount / 100,
      state.mobilityIndex / 100,
      state.coverageHoleCount / 50,
      state.interference,
      state.packetLoss,
      this.getSignalToInterferenceRatio(state),
      this.getLoadBalance(state),
      this.getHandoverFrequency(state)
    ];

    // Add skill context
    const skillContext = this.getSkillContextEncoding(skill);

    return tf.tensor2d([[...baseEncoding, ...skillContext]]);
  }

  private detectProblemType(state: RANState): number {
    // Detect primary problem type (0: energy, 1: capacity, 2: mobility, 3: coverage)
    if (state.energyConsumption > 150) return 0; // Energy problem
    if (state.userCount > 80 && state.throughput < 600) return 1; // Capacity problem
    if (state.mobilityIndex > 70) return 2; // Mobility problem
    if (state.coverageHoleCount > 10) return 3; // Coverage problem
    return Math.random() * 4; // No clear problem
  }

  private getUrgencyLevel(state: RANState): number {
    // Urgency level (0-1)
    let urgency = 0;

    if (state.signalStrength < -100) urgency += 0.4;
    if (state.latency > 80) urgency += 0.3;
    if (state.packetLoss > 0.05) urgency += 0.3;

    return Math.min(urgency, 1.0);
  }

  private getResourcePressure(state: RANState): number {
    // Resource pressure (0-1)
    return Math.min(state.userCount / 100, 1.0);
  }

  private getTrafficPattern(state: RANState): number {
    // Traffic pattern (0: low, 0.5: medium, 1: high)
    if (state.userCount < 30) return 0;
    if (state.userCount < 70) return 0.5;
    return 1;
  }

  private getEnvironmentalFactors(state: RANState): number {
    // Environmental factors affecting optimization
    return (state.interference + state.mobilityIndex / 100) / 2;
  }

  private getSkillContextEncoding(skill: string): number[] {
    // Encode current skill context
    const skills = ['energy_optimization', 'capacity_optimization', 'mobility_optimization', 'coverage_optimization'];
    const skillIndex = skills.indexOf(skill);

    return [
      skillIndex / 4, // Normalized skill index
      this.skillHierarchy[skill].subSkills.length / 10, // Number of sub-skills
      this.skillHierarchy[skill].timeout / 60000 // Timeout in minutes
    ];
  }

  private getSignalToInterferenceRatio(state: RANState): number {
    return Math.max(0, (state.signalStrength - (state.interference * 50)) / 100);
  }

  private getLoadBalance(state: RANState): number {
    // Load balance quality (0-1)
    const optimalLoad = 50;
    const deviation = Math.abs(state.userCount - optimalLoad) / optimalLoad;
    return Math.max(0, 1 - deviation);
  }

  private getHandoverFrequency(state: RANState): number {
    // Handover frequency (0-1)
    return Math.min(state.mobilityIndex / 100, 1.0);
  }

  private calculateEnergyReward(initialState: RANState, finalState: RANState): number {
    return (initialState.energyConsumption - finalState.energyConsumption) / initialState.energyConsumption;
  }

  private calculateThroughputReward(initialState: RANState, finalState: RANState): number {
    return (finalState.throughput - initialState.throughput) / initialState.throughput;
  }

  private calculateLatencyReward(initialState: RANState, finalState: RANState): number {
    return (initialState.latency - finalState.latency) / initialState.latency;
  }

  private calculateCoverageReward(initialState: RANState, finalState: RANState): number {
    const initialCoverage = Math.max(0, 1 - initialState.coverageHoleCount / 20);
    const finalCoverage = Math.max(0, 1 - finalState.coverageHoleCount / 20);
    return finalCoverage - initialCoverage;
  }

  private clampStateValues(state: RANState) {
    state.throughput = Math.max(50, Math.min(2000, state.throughput));
    state.latency = Math.max(5, Math.min(200, state.latency));
    state.signalStrength = Math.max(-120, Math.min(-50, state.signalStrength));
    state.energyConsumption = Math.max(20, Math.min(300, state.energyConsumption));
    state.userCount = Math.max(1, Math.min(200, state.userCount));
    state.mobilityIndex = Math.max(0, Math.min(100, state.mobilityIndex));
    state.coverageHoleCount = Math.max(0, Math.min(50, state.coverageHoleCount));
    state.interference = Math.max(0, Math.min(0.5, state.interference));
    state.packetLoss = Math.max(0, Math.min(0.2, state.packetLoss));
  }

  async trainHierarchicalPolicy(
    experiences: Array<HierarchicalExperience>
  ): Promise<HierarchicalTrainingMetrics> {
    let highLevelLoss = 0;
    let lowLevelLosses: { [skill: string]: number } = {};

    // Train high-level policy
    const highLevelTrainingData = experiences.map(exp => ({
      state: exp.initialState,
      skill: exp.selectedSkill,
      reward: exp.skillEvaluation.totalReward
    }));

    highLevelLoss = await this.trainHighLevelPolicy(highLevelTrainingData);

    // Train low-level policies
    for (const experience of experiences) {
      const skill = experience.selectedSkill;
      if (!lowLevelLosses[skill]) lowLevelLosses[skill] = 0;

      const lowLevelLoss = await this.trainLowLevelPolicy(skill, experience);
      lowLevelLosses[skill] += lowLevelLoss;
    }

    return {
      highLevelLoss,
      lowLevelLosses,
      totalExperiences: experiences.length
    };
  }

  private async trainHighLevelPolicy(trainingData: Array<{ state: RANState, skill: string, reward: number }>): Promise<number> {
    // Prepare training data
    const states = tf.tensor2d(trainingData.map(data => this.encodeHighLevelState(data.state).dataSync() as number[]));
    const skills = Object.keys(this.skillHierarchy);
    const skillIndices = trainingData.map(data => skills.indexOf(data.skill));
    const actions = tf.tensor1d(skillIndices, 'int32');

    // Train high-level policy
    const history = await this.highLevelPolicy.fit(states, actions, {
      epochs: 5,
      batchSize: 32,
      verbose: 0
    });

    // Cleanup
    states.dispose();
    actions.dispose();

    return history.history.loss?.[0] || 0;
  }

  private async trainLowLevelPolicy(skill: string, experience: HierarchicalExperience): Promise<number> {
    const policy = this.lowLevelPolicies.get(skill);
    if (!policy) return 0;

    // Prepare training data from skill execution
    const trainingData = experience.skillExecution.actions.map((action, index) => ({
      state: index === 0 ? experience.initialState : experience.skillExecution.steps[index - 1],
      actionIndex: skill === experience.selectedSkill ?
        this.skillHierarchy[skill].subSkills.indexOf(action.action) : 0
    }));

    if (trainingData.length === 0) return 0;

    const states = tf.tensor2d(trainingData.map(data => this.encodeLowLevelState(data.state, skill).dataSync() as number[]));
    const actions = tf.tensor1d(trainingData.map(data => data.actionIndex), 'int32');

    const history = await policy.fit(states, actions, {
      epochs: 3,
      batchSize: trainingData.length,
      verbose: 0
    });

    // Cleanup
    states.dispose();
    actions.dispose();

    return history.history.loss?.[0] || 0;
  }

  async storeHierarchicalExperience(experience: HierarchicalExperience) {
    const experienceData = {
      timestamp: Date.now(),
      initialState: experience.initialState,
      selectedSkill: experience.selectedSkill,
      skillConfidence: experience.skillConfidence,
      skillExecution: experience.skillExecution,
      skillEvaluation: experience.skillEvaluation,
      executionTime: experience.executionTime
    };

    const embedding = await computeEmbedding(JSON.stringify(experienceData));

    await this.agentDB.insertPattern({
      id: '',
      type: 'hierarchical-rl-experience',
      domain: 'ran-hierarchical-rl',
      pattern_data: JSON.stringify({ embedding, pattern: experienceData }),
      confidence: experience.skillConfidence,
      usage_count: 1,
      success_count: experience.success ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }
}

interface SkillHierarchy {
  [skillName: string]: {
    subSkills: string[];
    timeout: number;
    success_criteria: { [criteria: string]: number };
  };
}

interface HierarchicalExecution {
  selectedSkill: string;
  skillConfidence: number;
  lowLevelActions: Array<{ action: string, confidence: number, timestamp: number }>;
  skillEvaluation: SkillEvaluation;
  executionTime: number;
  totalReward: number;
  success: boolean;
}

interface SkillExecution {
  actions: Array<{ action: string, confidence: number, timestamp: number }>;
  totalReward: number;
  steps: number;
  finalState: RANState;
}

interface ActionResult {
  nextState: RANState;
  reward: number;
  done: boolean;
  action: string;
}

interface SkillEvaluation {
  success: boolean;
  successRate: number;
  totalReward: number;
  averageReward: number;
  rewards: {
    overall: number;
    energy: number;
    throughput: number;
    latency: number;
    coverage: number;
  };
  steps: number;
  efficiency: number;
  criteriaMet: number;
}

interface HierarchicalExperience {
  initialState: RANState;
  selectedSkill: string;
  skillConfidence: number;
  skillExecution: SkillExecution;
  skillEvaluation: SkillEvaluation;
}

interface HierarchicalTrainingMetrics {
  highLevelLoss: number;
  lowLevelLosses: { [skill: string]: number };
  totalExperiences: number;
}

class ProductionRANRLSystem {
  private agentDB: AgentDBAdapter;
  private multiObjectivePPO: RANMultiObjectivePPO;
  private hierarchicalRL: RANHierarchicalRL;
  private environment: RANEnvironment;
  private performanceTracker: RLPerformanceTracker;
  private modelManager: RLModelManager;

  async initialize() {
    await Promise.all([
      this.agentDB.initialize(),
      this.multiObjectivePPO.initialize(),
      this.hierarchicalRL.initialize(),
      this.environment.initialize()
    ]);

    this.performanceTracker = new RLPerformanceTracker();
    this.modelManager = new RLModelManager();

    await this.loadTrainedModels();
    await this.loadHistoricalPerformance();

    console.log('RAN RL Production System initialized');
  }

  async runProductionRLOptimization(state: RANState, strategy: 'multi-objective' | 'hierarchical' = 'multi-objective'): Promise<RANProductionResult> {
    const startTime = Date.now();
    const episodeId = this.generateEpisodeId();

    try {
      // Step 1: Select optimization strategy
      const selectedStrategy = await this.selectOptimizationStrategy(state, strategy);

      // Step 2: Execute RL optimization
      let rlResult: MultiObjectiveEvaluation | HierarchicalExecution;

      if (selectedStrategy === 'multi-objective') {
        rlResult = await this.executeMultiObjectivePPO(state);
      } else {
        rlResult = await this.executeHierarchicalRL(state);
      }

      // Step 3: Execute actions in environment
      const executionResult = await this.executeActions(state, rlResult);

      // Step 4: Update models with experience
      await this.updateModels(state, rlResult, executionResult);

      // Step 5: Track performance
      const performanceMetrics = this.calculatePerformanceMetrics(state, rlResult, executionResult, startTime);
      this.performanceTracker.recordEpisode(episodeId, selectedStrategy, performanceMetrics);

      // Step 6: Generate comprehensive report
      const report = await this.generateProductionReport(state, rlResult, executionResult, performanceMetrics);

      const result: RANProductionResult = {
        episodeId,
        strategy: selectedStrategy,
        initialState: state,
        rlResult,
        executionResult,
        performanceMetrics,
        report,
        timestamp: Date.now()
      };

      // Store result in AgentDB
      await this.storeProductionResult(result);

      return result;

    } catch (error) {
      console.error('RAN RL production optimization failed:', error);
      return this.generateFallbackResult(state, episodeId, startTime);
    }
  }

  private async selectOptimizationStrategy(state: RANState, preferredStrategy: string): Promise<'multi-objective' | 'hierarchical'> {
    // Use context-aware strategy selection
    const contextScore = this.calculateContextScore(state);

    // Multi-objective is better for balanced optimization
    // Hierarchical is better for complex, multi-step problems
    if (preferredStrategy === 'multi-objective') {
      return 'multi-objective';
    }

    if (contextScore.complexity > 0.7 || contextScore.multipleObjectives > 0.8) {
      return 'hierarchical';
    } else {
      return 'multi-objective';
    }
  }

  private calculateContextScore(state: RANState): { complexity: number, multipleObjectives: number, urgency: number } {
    return {
      complexity: Math.min((state.userCount / 100 + state.mobilityIndex / 100) / 2, 1.0),
      multipleObjectives: Math.min((
        (state.energyConsumption > 100 ? 0.25 : 0) +
        (state.throughput < 500 ? 0.25 : 0) +
        (state.latency > 50 ? 0.25 : 0) +
        (state.coverageHoleCount > 10 ? 0.25 : 0)
      ), 1.0),
      urgency: Math.min((
        (state.signalStrength < -90 ? 0.4 : 0) +
        (state.latency > 80 ? 0.3 : 0) +
        (state.packetLoss > 0.03 ? 0.3 : 0)
      ), 1.0)
    };
  }

  private async executeMultiObjectivePPO(state: RANState): Promise<MultiObjectiveEvaluation> {
    // Use the trained PPO policy for optimization
    const testStates = [state]; // Single state for production

    const evaluation = await this.multiObjectivePPO.evaluateMultiObjectivePolicy(testStates);

    return evaluation;
  }

  private async executeHierarchicalRL(state: RANState): Promise<HierarchicalExecution> {
    // Use hierarchical RL for complex optimization
    const execution = await this.hierarchicalRL.executeHierarchicalPolicy(state);

    return execution;
  }

  private async executeActions(state: RANState, rlResult: MultiObjectiveEvaluation | HierarchicalExecution): Promise<ActionExecutionResult> {
    const startTime = Date.now();
    let totalReward = 0;
    const executedActions: Array<{ action: string, reward: number, timestamp: number }> = [];

    let currentState = state;

    if ('evaluations' in rlResult) {
      // Multi-objective PPO result
      const evaluation = rlResult.evaluations[0];
      const actionName = this.multiObjectivePPO.getActionName(evaluation.action);

      const result = await this.environment.step(evaluation.action);
      totalReward = result.reward;
      executedActions.push({
        action: actionName,
        reward: result.reward,
        timestamp: Date.now()
      });

      currentState = result.nextState;

    } else {
      // Hierarchical RL result
      for (const actionInfo of rlResult.lowLevelActions) {
        const actionIndex = this.getActionIndex(actionInfo.action, rlResult.selectedSkill);
        const result = await this.environment.step(actionIndex);

        totalReward += result.reward;
        executedActions.push({
          action: actionInfo.action,
          reward: result.reward,
          timestamp: Date.now()
        });

        currentState = result.nextState;

        if (result.done) break;
      }
    }

    return {
      success: totalReward > 0,
      totalReward,
      executedActions,
      finalState: currentState,
      executionTime: Date.now() - startTime,
      improvement: this.calculateImprovement(state, currentState)
    };
  }

  private getActionIndex(actionName: string, skill: string): number {
    const skillConfig = this.hierarchicalRL['skillHierarchy'][skill];
    return skillConfig.subSkills.indexOf(actionName);
  }

  private async updateModels(
    initialState: RANState,
    rlResult: MultiObjectiveEvaluation | HierarchicalExecution,
    executionResult: ActionExecutionResult
  ) {
    // Store experience for model updates
    if ('evaluations' in rlResult) {
      // Multi-objective PPO experience
      const evaluation = rlResult.evaluations[0];
      await this.multiObjectivePPO.storeExperience(
        initialState,
        evaluation.action,
        evaluation.rewards,
        executionResult.finalState,
        0, // logProb (not available from evaluation)
        0, // value (not available from evaluation)
        executionResult.success
      );

      // Train PPO if enough experiences
      await this.multiObjectivePPO.trainPPOEpoch(5, 32);

    } else {
      // Hierarchical RL experience
      const hierarchicalExperience: HierarchicalExperience = {
        initialState,
        selectedSkill: rlResult.selectedSkill,
        skillConfidence: rlResult.skillConfidence,
        skillExecution: {
          actions: rlResult.lowLevelActions,
          totalReward: executionResult.totalReward,
          steps: rlResult.lowLevelActions.length,
          finalState: executionResult.finalState
        },
        skillEvaluation: rlResult.skillEvaluation
      };

      await this.hierarchicalRL.storeHierarchicalExperience(hierarchicalExperience);
    }
  }

  private calculatePerformanceMetrics(
    initialState: RANState,
    rlResult: MultiObjectiveEvaluation | HierarchicalExecution,
    executionResult: ActionExecutionResult,
    startTime: number
  ): RLPerformanceMetrics {
    const executionTime = Date.now() - startTime;
    const improvement = this.calculateImprovement(initialState, executionResult.finalState);

    let objectivePerformance: { [objective: string]: number } = {};

    if ('averageRewards' in rlResult) {
      // Multi-objective PPO metrics
      objectivePerformance = rlResult.averageRewards;
    } else {
      // Hierarchical RL metrics
      objectivePerformance = rlResult.skillEvaluation.rewards;
    }

    return {
      executionTime,
      totalReward: executionResult.totalReward,
      improvement,
      success: executionResult.success,
      objectivePerformance,
      strategy: 'evaluations' in rlResult ? 'multi-objective' : 'hierarchical',
      convergence: this.calculateConvergence(executionResult),
      efficiency: this.calculateEfficiency(executionResult, executionTime)
    };
  }

  private calculateImprovement(initialState: RANState, finalState: RANState): number {
    // Weighted improvement calculation
    const weights = {
      throughput: 0.3,
      latency: -0.25,
      energyConsumption: -0.2,
      signalStrength: 0.15,
      coverage: 0.1
    };

    let totalImprovement = 0;

    for (const [kpi, weight] of Object.entries(weights)) {
      const initial = initialState[kpi] || 0;
      const final = finalState[kpi] || 0;

      if (initial > 0) {
        const change = (final - initial) / initial;
        totalImprovement += change * Math.abs(weight);
      }
    }

    return totalImprovement;
  }

  private calculateConvergence(executionResult: ActionExecutionResult): number {
    // Convergence based on reward stability
    if (executionResult.executedActions.length < 2) return 0.5;

    const rewards = executionResult.executedActions.map(action => action.reward);
    const avgReward = rewards.reduce((sum, r) => sum + r, 0) / rewards.length;
    const variance = rewards.reduce((sum, r) => sum + Math.pow(r - avgReward, 2), 0) / rewards.length;

    // Low variance indicates convergence
    return Math.max(0, 1 - variance);
  }

  private calculateEfficiency(executionResult: ActionExecutionResult, executionTime: number): number {
    // Efficiency based on reward per time
    const rewardPerMs = executionResult.totalReward / executionTime;
    return Math.min(rewardPerMs * 1000, 1.0); // Normalize to 0-1
  }

  private async generateProductionReport(
    state: RANState,
    rlResult: MultiObjectiveEvaluation | HierarchicalExecution,
    executionResult: ActionExecutionResult,
    metrics: RLPerformanceMetrics
  ): Promise<string> {
    const report = `
RAN RL Production Optimization Report
====================================

Episode ID: ${this.generateEpisodeId()}
Timestamp: ${new Date().toISOString()}
Strategy: ${metrics.strategy}

Initial State:
- Throughput: ${state.throughput.toFixed(1)} Mbps
- Latency: ${state.latency.toFixed(1)} ms
- Signal Strength: ${state.signalStrength.toFixed(1)} dBm
- Energy Consumption: ${state.energyConsumption.toFixed(1)} W
- User Count: ${state.userCount}
- Mobility Index: ${state.mobilityIndex.toFixed(1)}
- Coverage Holes: ${state.coverageHoleCount}

RL Strategy Analysis:
${this.generateRLAnalysis(rlResult)}

Executed Actions:
${executionResult.executedActions.map((action, i) =>
  `  ${i + 1}. ${action.action} (Reward: ${action.reward.toFixed(3)}, Confidence: ${(action.confidence || 0).toFixed(2)})`
).join('\n')}

Performance Metrics:
- Execution Time: ${metrics.executionTime} ms
- Total Reward: ${metrics.totalReward.toFixed(3)}
- Overall Improvement: ${(metrics.improvement * 100).toFixed(1)}%
- Success: ${metrics.success ? 'Yes' : 'No'}
- Convergence: ${(metrics.convergence * 100).toFixed(1)}%
- Efficiency: ${(metrics.efficiency * 100).toFixed(1)}%

Objective Performance:
${Object.entries(metrics.objectivePerformance).map(([objective, performance]) =>
  `  ${objective}: ${(performance * 100).toFixed(1)}%`
).join('\n')}

Final State:
- Throughput: ${executionResult.finalState.throughput.toFixed(1)} Mbps
- Latency: ${executionResult.finalState.latency.toFixed(1)} ms
- Signal Strength: ${executionResult.finalState.signalStrength.toFixed(1)} dBm
- Energy Consumption: ${executionResult.finalState.energyConsumption.toFixed(1)} W

Learning Insights:
${this.generateLearningInsights(rlResult, metrics)}

Recommendations:
${this.generateRecommendations(rlResult, metrics).map(rec => `  - ${rec}`).join('\n')}

System Performance:
${this.performanceTracker.getRecentPerformanceSummary()}

Next Optimization Cycle: ${new Date(Date.now() + 60000).toISOString()}
    `.trim();

    return report;
  }

  private generateRLAnalysis(rlResult: MultiObjectiveEvaluation | HierarchicalExecution): string {
    if ('evaluations' in rlResult) {
      // Multi-objective PPO analysis
      const evaluation = rlResult.evaluations[0];
      return `
Multi-Objective PPO Analysis:
- Selected Action: ${this.multiObjectivePPO.getActionName(evaluation.action)}
- Expected Rewards: ${Object.entries(evaluation.rewards).map(([obj, rew]) => `${obj}: ${rew.toFixed(3)}`).join(', ')}
- Total Expected Reward: ${evaluation.totalReward.toFixed(3)}
- Objective Performance: ${Object.entries(rlResult.objectivePerformance).map(([obj, perf]) => `${obj}: ${perf.status} (${(perf.score * 100).toFixed(1)}%)`).join(', ')}
      `.trim();
    } else {
      // Hierarchical RL analysis
      return `
Hierarchical RL Analysis:
- Selected Skill: ${rlResult.selectedSkill}
- Skill Confidence: ${(rlResult.skillConfidence * 100).toFixed(1)}%
- Low-Level Actions: ${rlResult.lowLevelActions.length} executed
- Skill Success: ${rlResult.success ? 'Yes' : 'No'}
- Success Rate: ${(rlResult.skillEvaluation.successRate * 100).toFixed(1)}%
- Total Reward: ${rlResult.skillEvaluation.totalReward.toFixed(3)}
- Average Reward: ${rlResult.skillEvaluation.averageReward.toFixed(3)}
- Efficiency: ${rlResult.skillEvaluation.efficiency.toFixed(2)} actions per expected
      `.trim();
    }
  }

  private generateLearningInsights(rlResult: MultiObjectiveEvaluation | HierarchicalExecution, metrics: RLPerformanceMetrics): string[] {
    const insights: string[] = [];

    if (metrics.success) {
      insights.push('RL optimization successfully achieved target improvements');
    }

    if (metrics.convergence > 0.8) {
      insights.push('Good convergence achieved - policy is stable');
    }

    if (metrics.efficiency > 0.7) {
      insights.push('High efficiency optimization - fast reward achievement');
    }

    if ('evaluations' in rlResult) {
      const evaluation = rlResult.evaluations[0];
      const dominantObjective = Object.entries(evaluation.rewards)
        .reduce((max, [obj, rew]) => rew > max.value ? { objective: obj, value: rew } : max, { objective: '', value: -Infinity });

      insights.push(`Dominant optimization objective: ${dominantObjective.objective} (reward: ${dominantObjective.value.toFixed(3)})`);
    } else {
      insights.push(`Hierarchical skill ${rlResult.selectedSkill} completed with ${rlResult.lowLevelActions.length} actions`);
    }

    if (metrics.improvement > 0.15) {
      insights.push('Exceptional improvement achieved - policy performing excellently');
    }

    return insights;
  }

  private generateRecommendations(rlResult: MultiObjectiveEvaluation | HierarchicalExecution, metrics: RLPerformanceMetrics): string[] {
    const recommendations: string[] = [];

    if (metrics.success && metrics.improvement > 0.1) {
      recommendations.push('Continue current optimization strategy - performing well');
    }

    if (metrics.convergence < 0.5) {
      recommendations.push('Policy showing poor convergence - consider additional training');
    }

    if (metrics.efficiency < 0.5) {
      recommendations.push('Low efficiency - optimize action selection and execution');
    }

    if ('evaluations' in rlResult) {
      // Multi-objective recommendations
      const poorObjectives = Object.entries(rlResult.objectivePerformance)
        .filter(([, perf]) => perf.status === 'poor')
        .map(([obj]) => obj);

      if (poorObjectives.length > 0) {
        recommendations.push(`Focus on improving ${poorObjectives.join(', ')} objectives`);
      }
    } else {
      // Hierarchical RL recommendations
      if (rlResult.skillEvaluation.successRate < 0.5) {
        recommendations.push(`Improve ${rlResult.selectedSkill} execution - low success rate`);
      }

      if (rlResult.skillEvaluation.efficiency > 1.5) {
        recommendations.push('Skill execution using too many actions - optimize skill hierarchy');
      }
    }

    return recommendations;
  }

  private async storeProductionResult(result: RANProductionResult) {
    const resultData = {
      episodeId: result.episodeId,
      strategy: result.strategy,
      initialState: result.initialState,
      rlResult: result.rlResult,
      executionResult: result.executionResult,
      performanceMetrics: result.performanceMetrics,
      timestamp: result.timestamp
    };

    const embedding = await computeEmbedding(JSON.stringify(resultData));

    await this.agentDB.insertPattern({
      id: '',
      type: 'ran-rl-production-result',
      domain: 'ran-reinforcement-learning-production',
      pattern_data: JSON.stringify({ embedding, pattern: resultData }),
      confidence: result.performanceMetrics.success ? 0.9 : 0.5,
      usage_count: 1,
      success_count: result.performanceMetrics.success ? 1 : 0,
      created_at: Date.now(),
      last_used: Date.now(),
    });
  }

  private generateEpisodeId(): string {
    return `ran-rl-${Date.now()}-${Math.random().toString(36).substr(2, 9)}`;
  }

  private generateFallbackResult(state: RANState, episodeId: string, startTime: number): RANProductionResult {
    return {
      episodeId,
      strategy: 'fallback',
      initialState: state,
      rlResult: null,
      executionResult: {
        success: false,
        totalReward: 0,
        executedActions: [],
        finalState: state,
        executionTime: Date.now() - startTime,
        improvement: 0
      },
      performanceMetrics: {
        executionTime: Date.now() - startTime,
        totalReward: 0,
        improvement: 0,
        success: false,
        objectivePerformance: {},
        strategy: 'fallback',
        convergence: 0,
        efficiency: 0
      },
      report: 'RAN RL production optimization failed - fallback to monitoring',
      timestamp: Date.now()
    };
  }

  private async loadTrainedModels() {
    // Load trained models from AgentDB or files
    try {
      const modelEmbedding = await computeEmbedding('trained-ran-models');
      const result = await this.agentDB.retrieveWithReasoning(modelEmbedding, {
        domain: 'ran-reinforcement-learning',
        k: 1,
        filters: { type: 'trained-model' }
      });

      if (result.memories.length > 0) {
        console.log('Loaded trained models from AgentDB');
        // Would actually load model weights here
      }
    } catch (error) {
      console.log('No trained models found, starting with fresh models');
    }
  }

  private async loadHistoricalPerformance() {
    // Load historical performance data
    try {
      const performanceEmbedding = await computeEmbedding('rl-performance-history');
      const result = await this.agentDB.retrieveWithReasoning(performanceEmbedding, {
        domain: 'ran-reinforcement-learning-production',
        k: 1000
      });

      this.performanceTracker.loadHistoricalData(result.memories.map(m => m.pattern));
      console.log(`Loaded ${result.memories.length} historical performance records`);
    } catch (error) {
      console.log('No historical performance data found');
    }
  }

  async generateSystemPerformanceReport(): Promise<string> {
    const recentPerformance = this.performanceTracker.getRecentPerformance(100);
    const systemHealth = this.assessSystemHealth(recentPerformance);

    return `
RAN RL System Performance Report
===============================

Report Generated: ${new Date().toISOString()}

Overall Performance (Last 100 Episodes):
- Success Rate: ${(recentPerformance.successRate * 100).toFixed(1)}%
- Average Improvement: ${(recentPerformance.avgImprovement * 100).toFixed(1)}%
- Average Execution Time: ${recentPerformance.avgExecutionTime.toFixed(1)}ms
- Average Convergence: ${(recentPerformance.avgConvergence * 100).toFixed(1)}%
- Average Efficiency: ${(recentPerformance.avgEfficiency * 100).toFixed(1)}%

Strategy Performance:
${this.getStrategyPerformanceSummary(recentPerformance)}

Objective Performance:
${this.getObjectivePerformanceSummary(recentPerformance)}

System Health:
${systemHealth.map(health => `  ${health}`).join('\n')}

Recent Trends:
${this.analyzeTrends(recentPerformance).map(trend => `  • ${trend}`).join('\n')}

Learning Progress:
${this.analyzeLearningProgress(recentPerformance).map(progress => `  • ${progress}`).join('\n')}

Model Performance:
${this.analyzeModelPerformance(recentPerformance).map(performance => `  • ${performance}`).join('\n')}

Recommendations:
${this.generateSystemRecommendations(recentPerformance, systemHealth).map(rec => `  ${rec}`).join('\n')}
    `.trim();
  }

  private assessSystemHealth(recentPerformance: any): string[] {
    const health: string[] = [];

    if (recentPerformance.successRate > 0.85) {
      health.push('✅ High success rate - system performing excellently');
    } else if (recentPerformance.successRate > 0.7) {
      health.push('⚠️ Moderate success rate - room for improvement');
    } else {
      health.push('❌ Low success rate - immediate attention needed');
    }

    if (recentPerformance.avgImprovement > 0.1) {
      health.push('✅ Good average improvement - policies effective');
    } else {
      health.push('⚠️ Low improvement - review optimization strategies');
    }

    if (recentPerformance.avgExecutionTime < 200) {
      health.push('✅ Fast execution times - efficient optimization');
    } else {
      health.push('⚠️ Slow execution times - optimize inference');
    }

    if (recentPerformance.avgConvergence > 0.7) {
      health.push('✅ Good convergence - stable policies');
    } else {
      health.push('⚠️ Poor convergence - policy instability detected');
    }

    return health;
  }

  private getStrategyPerformanceSummary(recentPerformance: any): string {
    const strategyStats = recentPerformance.strategyStats || {};

    return Object.entries(strategyStats)
      .map(([strategy, stats]: [string, any]) =>
        `  ${strategy}: ${(stats.successRate * 100).toFixed(1)}% success, ${(stats.avgImprovement * 100).toFixed(1)}% avg improvement`
      )
      .join('\n');
  }

  private getObjectivePerformanceSummary(recentPerformance: any): string {
    const objectiveStats = recentPerformance.objectiveStats || {};

    return Object.entries(objectiveStats)
      .map(([objective, stats]: [string, any]) =>
        `  ${objective}: ${(stats.avgPerformance * 100).toFixed(1)}% average performance`
      )
      .join('\n');
  }

  private analyzeTrends(recentPerformance: any): string[] {
    const trends: string[] = [];

    // Analyze success rate trend
    if (recentPerformance.successRateTrend > 0.05) {
      trends.push('Success rate improving over time');
    } else if (recentPerformance.successRateTrend < -0.05) {
      trends.push('Success rate declining - investigate causes');
    }

    // Analyze improvement trend
    if (recentPerformance.improvementTrend > 0.02) {
      trends.push('Optimization improvement increasing');
    } else if (recentPerformance.improvementTrend < -0.02) {
      trends.push('Optimization quality decreasing');
    }

    // Analyze strategy preference trends
    const preferredStrategy = this.getPreferredStrategy(recentPerformance);
    if (preferredStrategy) {
      trends.push(`Preferred strategy: ${preferredStrategy}`);
    }

    return trends;
  }

  private analyzeLearningProgress(recentPerformance: any): string[] {
    const progress: string[] = [];

    if (recentPerformance.convergenceTrend > 0) {
      progress.push('Policy convergence improving');
    }

    if (recentPerformance.efficiencyTrend > 0) {
      progress.push('Optimization efficiency increasing');
    }

    if (recentPerformance.learningRate > 0.8) {
      progress.push('Fast learning rate - policies adapting quickly');
    } else if (recentPerformance.learningRate < 0.3) {
      progress.push('Slow learning rate - may need more data or adjusted hyperparameters');
    }

    return progress;
  }

  private analyzeModelPerformance(recentPerformance: any): string[] {
    const performance: string[] = [];

    if (recentPerformance.modelStability > 0.8) {
      performance.push('Models showing stable performance');
    }

    if (recentPerformance.predictionAccuracy > 0.85) {
      performance.push('High prediction accuracy - models well-trained');
    }

    if (recentPerformance.explorationBalance > 0.7) {
      performance.push('Good exploration-exploitation balance');
    }

    return performance;
  }

  private getPreferredStrategy(recentPerformance: any): string | null {
    const strategyStats = recentPerformance.strategyStats || {};
    let bestStrategy = null;
    let bestScore = -Infinity;

    for (const [strategy, stats] of Object.entries(strategyStats)) {
      const score = (stats as any).successRate * (stats as any).avgImprovement;
      if (score > bestScore) {
        bestScore = score;
        bestStrategy = strategy;
      }
    }

    return bestStrategy;
  }

  private generateSystemRecommendations(recentPerformance: any, health: string[]): string[] {
    const recommendations: string[] = [];

    if (recentPerformance.successRate < 0.7) {
      recommendations.push('Increase training data diversity and volume');
      recommendations.push('Review reward function design');
    }

    if (recentPerformance.avgImprovement < 0.05) {
      recommendations.push('Adjust optimization objectives and weights');
      recommendations.push('Consider environment parameter tuning');
    }

    if (recentPerformance.avgConvergence < 0.5) {
      recommendations.push('Reduce learning rate or increase batch size');
      recommendations.push('Review policy network architecture');
    }

    if (health.some(h => h.includes('❌'))) {
      recommendations.push('Immediate system review required - critical issues detected');
    }

    return recommendations;
  }
}

interface RANProductionResult {
  episodeId: string;
  strategy: 'multi-objective' | 'hierarchical';
  initialState: RANState;
  rlResult: MultiObjectiveEvaluation | HierarchicalExecution | null;
  executionResult: ActionExecutionResult;
  performanceMetrics: RLPerformanceMetrics;
  report: string;
  timestamp: number;
}

interface ActionExecutionResult {
  success: boolean;
  totalReward: number;
  executedActions: Array<{ action: string, reward: number, timestamp: number, confidence?: number }>;
  finalState: RANState;
  executionTime: number;
  improvement: number;
}

interface RLPerformanceMetrics {
  executionTime: number;
  totalReward: number;
  improvement: number;
  success: boolean;
  objectivePerformance: { [objective: string]: number };
  strategy: 'multi-objective' | 'hierarchical';
  convergence: number;
  efficiency: number;
}

class RLPerformanceTracker {
  private historicalData: Array<any> = [];

  recordEpisode(episodeId: string, strategy: string, metrics: RLPerformanceMetrics) {
    const record = {
      episodeId,
      strategy,
      timestamp: Date.now(),
      metrics
    };

    this.historicalData.push(record);

    // Keep only last 1000 records
    if (this.historicalData.length > 1000) {
      this.historicalData = this.historicalData.slice(-1000);
    }
  }

  getRecentPerformance(count: number = 100): any {
    const recent = this.historicalData.slice(-count);

    if (recent.length === 0) {
      return {
        successRate: 0,
        avgImprovement: 0,
        avgExecutionTime: 0,
        avgConvergence: 0,
        avgEfficiency: 0,
        strategyStats: {},
        objectiveStats: {},
        trends: {}
      };
    }

    const successful = recent.filter(r => r.metrics.success);
    const totalImprovement = recent.reduce((sum, r) => sum + r.metrics.improvement, 0);
    const totalTime = recent.reduce((sum, r) => sum + r.metrics.executionTime, 0);
    const totalConvergence = recent.reduce((sum, r) => sum + r.metrics.convergence, 0);
    const totalEfficiency = recent.reduce((sum, r) => sum + r.metrics.efficiency, 0);

    // Strategy statistics
    const strategyStats: { [strategy: string]: { successRate: number, avgImprovement: number } } = {};
    const strategyGroups = this.groupBy(recent, 'strategy');

    for (const [strategy, episodes] of Object.entries(strategyGroups)) {
      const successfulEpisodes = episodes.filter((e: any) => e.metrics.success);
      const strategyImprovement = episodes.reduce((sum: number, e: any) => sum + e.metrics.improvement, 0);

      strategyStats[strategy] = {
        successRate: successfulEpisodes.length / episodes.length,
        avgImprovement: strategyImprovement / episodes.length
      };
    }

    // Objective statistics
    const objectiveStats: { [objective: string]: { avgPerformance: number } } = {};
    const objectiveAccumulator: { [objective: string]: { total: number, count: number } } = {};

    recent.forEach(episode => {
      for (const [objective, performance] of Object.entries(episode.metrics.objectivePerformance)) {
        if (!objectiveAccumulator[objective]) {
          objectiveAccumulator[objective] = { total: 0, count: 0 };
        }
        objectiveAccumulator[objective].total += performance;
        objectiveAccumulator[objective].count += 1;
      }
    });

    for (const [objective, data] of Object.entries(objectiveAccumulator)) {
      objectiveStats[objective] = {
        avgPerformance: data.total / data.count
      };
    }

    // Calculate trends
    const midpoint = Math.floor(recent.length / 2);
    const firstHalf = recent.slice(0, midpoint);
    const secondHalf = recent.slice(midpoint);

    const firstHalfSuccess = firstHalf.filter(r => r.metrics.success).length / firstHalf.length;
    const secondHalfSuccess = secondHalf.filter(r => r.metrics.success).length / secondHalf.length;
    const successRateTrend = secondHalfSuccess - firstHalfSuccess;

    const firstHalfImprovement = firstHalf.reduce((sum, r) => sum + r.metrics.improvement, 0) / firstHalf.length;
    const secondHalfImprovement = secondHalf.reduce((sum, r) => sum + r.metrics.improvement, 0) / secondHalf.length;
    const improvementTrend = secondHalfImprovement - firstHalfImprovement;

    return {
      successRate: successful.length / recent.length,
      avgImprovement: totalImprovement / recent.length,
      avgExecutionTime: totalTime / recent.length,
      avgConvergence: totalConvergence / recent.length,
      avgEfficiency: totalEfficiency / recent.length,
      strategyStats,
      objectiveStats,
      successRateTrend,
      improvementTrend,
      convergenceTrend: this.calculateTrend(firstHalf, secondHalf, 'convergence'),
      efficiencyTrend: this.calculateTrend(firstHalf, secondHalf, 'efficiency'),
      learningRate: this.calculateLearningRate(recent),
      modelStability: this.calculateModelStability(recent),
      predictionAccuracy: this.calculatePredictionAccuracy(recent),
      explorationBalance: this.calculateExplorationBalance(recent)
    };
  }

  private groupBy(array: any[], key: string): { [key: string]: any[] } {
    return array.reduce((groups, item) => {
      const group = item[key];
      if (!groups[group]) groups[group] = [];
      groups[group].push(item);
      return groups;
    }, {});
  }

  private calculateTrend(firstHalf: any[], secondHalf: any[], metric: string): number {
    const firstAvg = firstHalf.reduce((sum, item) => sum + item.metrics[metric], 0) / firstHalf.length;
    const secondAvg = secondHalf.reduce((sum, item) => sum + item.metrics[metric], 0) / secondHalf.length;
    return secondAvg - firstAvg;
  }

  private calculateLearningRate(recent: any[]): number {
    // Simplified learning rate calculation based on improvement velocity
    if (recent.length < 10) return 0.5;

    const improvements = recent.slice(-10).map(r => r.metrics.improvement);
    const variance = this.calculateVariance(improvements);
    const avgImprovement = improvements.reduce((sum, imp) => sum + imp, 0) / improvements.length;

    // High variance with good average improvement indicates fast learning
    return Math.min(1, (avgImprovement + variance) / 2);
  }

  private calculateModelStability(recent: any[]): number {
    if (recent.length < 20) return 0.5;

    const executionTimes = recent.slice(-20).map(r => r.metrics.executionTime);
    const variance = this.calculateVariance(executionTimes);
    const avgTime = executionTimes.reduce((sum, time) => sum + time, 0) / executionTimes.length;

    // Low variance relative to average indicates stability
    return Math.max(0, 1 - (variance / avgTime));
  }

  private calculatePredictionAccuracy(recent: any[]): number {
    // Simplified prediction accuracy based on success rate and convergence
    const successRate = recent.filter(r => r.metrics.success).length / recent.length;
    const avgConvergence = recent.reduce((sum, r) => sum + r.metrics.convergence, 0) / recent.length;

    return (successRate + avgConvergence) / 2;
  }

  private calculateExplorationBalance(recent: any[]): number {
    // Simplified exploration balance based on strategy diversity
    const strategyCounts: { [strategy: string]: number } = {};

    recent.forEach(episode => {
      strategyCounts[episode.strategy] = (strategyCounts[episode.strategy] || 0) + 1;
    });

    const totalEpisodes = recent.length;
    const maxCount = Math.max(...Object.values(strategyCounts));

    // Good balance if no strategy dominates too much
    return 1 - (maxCount / totalEpisodes);
  }

  private calculateVariance(values: number[]): number {
    if (values.length === 0) return 0;

    const mean = values.reduce((sum, val) => sum + val, 0) / values.length;
    return values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
  }

  loadHistoricalData(data: any[]) {
    this.historicalData = data;
  }

  getRecentPerformanceSummary(): string {
    const recent = this.getRecentPerformance(50);

    return `
Recent 50 Episodes Summary:
- Success Rate: ${(recent.successRate * 100).toFixed(1)}%
- Average Improvement: ${(recent.avgImprovement * 100).toFixed(1)}%
- Average Execution Time: ${recent.avgExecutionTime.toFixed(1)}ms
- Convergence: ${(recent.avgConvergence * 100).toFixed(1)}%
- Efficiency: ${(recent.avgEfficiency * 100).toFixed(1)}%
    `.trim();
  }
}

class RLModelManager {
  // Model management functionality
  // Would include model versioning, checkpointing, etc.
}

const rlSystem = new ProductionRANRLSystem();
await rlSystem.initialize();

// Run production optimization
const currentState = {
  throughput: 600,
  latency: 45,
  packetLoss: 0.03,
  signalStrength: -75,
  interference: 0.12,
  energyConsumption: 95,
  userCount: 65,
  mobilityIndex: 45,
  coverageHoleCount: 8
};

const result = await rlSystem.runProductionRLOptimization(currentState, 'multi-objective');
console.log(`Strategy: ${result.strategy}`);
console.log(`Success: ${result.performanceMetrics.success}`);
console.log(`Improvement: ${(result.performanceMetrics.improvement * 100).toFixed(1)}%`);
console.log(`Execution time: ${result.performanceMetrics.executionTime}ms`);

const ppo = new RANMultiObjectivePPO();
await ppo.initialize();

const testStates = [/* array of test states */];
const evaluation = await ppo.evaluateMultiObjectivePolicy(testStates);

console.log('Average rewards by objective:');
Object.entries(evaluation.averageRewards).forEach(([objective, reward]) => {
  console.log(`  ${objective}: ${reward.toFixed(3)}`);
});

console.log('Objective performance:');
Object.entries(evaluation.objectivePerformance).forEach(([objective, perf]) => {
  console.log(`  ${objective}: ${perf.status} (${(perf.score * 100).toFixed(1)}%)`);
});

const hierarchicalRL = new RANHierarchicalRL();
await hierarchicalRL.initialize();

const state = { /* RAN state */ };
const execution = await hierarchicalRL.executeHierarchicalPolicy(state);

console.log(`Selected skill: ${execution.selectedSkill}`);
console.log(`Skill confidence: ${(execution.skillConfidence * 100).toFixed(1)}%`);
console.log(`Actions executed: ${execution.lowLevelActions.length}`);
console.log(`Total reward: ${execution.skillEvaluation.totalReward.toFixed(3)}`);
console.log(`Success: ${execution.success ? 'Yes' : 'No'}`);

# RAN RL Configuration
export RAN_RL_DB_PATH=.agentdb/ran-rl.db
export RAN_RL_MODEL_PATH=./models
export RAN_RL_LOG_LEVEL=info

# TensorFlow Configuration
export TF_CPP_MIN_LOG_LEVEL=2
export RAN_RL_GPU_ACCELERATION=true

# AgentDB Configuration
export AGENTDB_ENABLED=true
export AGENTDB_QUANTIZATION=scalar
export AGENTDB_CACHE_SIZE=3000

# RL Training Configuration
export RAN_RL_LEARNING_RATE=0.0001
export RAN_RL_BATCH_SIZE=64
export RAN_RL_EPISODES=1000
export RAN_RL_DISCOUNT_FACTOR=0.99

# Performance Optimization
export RAN_RL_ENABLE_CACHING=true
export RAN_RL_PARALLEL_TRAINING=true
export RAN_RL_EXPERIENCE_REPLAY_SIZE=10000
export RAN_RL_UPDATE_FREQUENCY=4

// Adjust hyperparameters
const optimizer = tf.train.adam(0.0001); // Lower learning rate
const batchSize = 128; // Larger batch size
const epochs = 50; // More epochs per update

// Adjust objective weights
const objectives = {
  energy: { weight: 0.4, target: 'minimize' },    // Increase energy weight
  throughput: { weight: 0.2, target: 'maximize' }, // Reduce throughput weight
  // ... other objectives
};

// Add skill validation
if (!skillHierarchy[selectedSkill]) {
  console.error(`Invalid skill selected: ${selectedSkill}`);
  return fallbackToDefaultSkill();
}

class EricssonRLIntegration {
  private rlSystem: ProductionRANRLSystem;

  async integrateWithEricssonRAN() {
    // Continuous RL optimization loop
    setInterval(async () => {
      const currentKPIs = await this.getEricssonRANKPIs();
      const optimization = await this.rlSystem.runProductionRLOptimization(currentKPIs);

      if (optimization.performanceMetrics.success) {
        await this.applyRLOptimization(optimization);
      }

      // Update models periodically
      if (Math.random() < 0.1) { // 10% chance
        await this.rlSystem.generateSystemPerformanceReport();
      }
    }, 45000); // Every 45 seconds
  }

  private async getEricssonRANKPIs(): Promise<RANState> {
    // Fetch from Ericsson RAN monitoring APIs
    const response = await fetch('/api/ericsson/ran/kpis/current');
    const data = await response.json();
    return this.convertEricssonData(data);
  }

  private async applyRLOptimization(result: RANProductionResult) {
    const actions = result.executionResult.executedActions;

    for (const action of actions) {
      await fetch('/api/ericsson/ran/optimize', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          action: action.action,
          parameters: this.extractActionParameters(action),
          confidence: action.confidence || 0.8
        })
      });
    }
  }
}