claude-flow
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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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text/typescript
/**
* Proximal Policy Optimization (PPO)
*
* Implements PPO algorithm for stable policy learning with:
* - Clipped surrogate objective
* - GAE (Generalized Advantage Estimation)
* - Value function clipping
* - Entropy bonus
*
* Performance Target: <10ms per update step
*/
import type {
PPOConfig,
Trajectory,
TrajectoryStep,
} from '../types.js';
/**
* Default PPO configuration
*/
export const DEFAULT_PPO_CONFIG: PPOConfig = {
algorithm: 'ppo',
learningRate: 0.0003,
gamma: 0.99,
entropyCoef: 0.01,
valueLossCoef: 0.5,
maxGradNorm: 0.5,
epochs: 4,
miniBatchSize: 64,
clipRange: 0.2,
clipRangeVf: null,
targetKL: 0.01,
gaeLambda: 0.95,
};
/**
* PPO experience buffer entry
*/
interface PPOExperience {
state: Float32Array;
action: number;
reward: number;
value: number;
logProb: number;
advantage: number;
return_: number;
}
/**
* PPO Algorithm Implementation
*/
export class PPOAlgorithm {
private config: PPOConfig;
// Policy network weights (simplified linear model for speed)
private policyWeights: Float32Array;
private valueWeights: Float32Array;
// Optimizer state
private policyMomentum: Float32Array;
private valueMomentum: Float32Array;
// Experience buffer
private buffer: PPOExperience[] = [];
// Statistics
private updateCount = 0;
private totalLoss = 0;
private approxKL = 0;
private clipFraction = 0;
constructor(config: Partial<PPOConfig> = {}) {
this.config = { ...DEFAULT_PPO_CONFIG, ...config };
// Initialize weights (768 input dim, simplified)
const dim = 768;
this.policyWeights = new Float32Array(dim);
this.valueWeights = new Float32Array(dim);
this.policyMomentum = new Float32Array(dim);
this.valueMomentum = new Float32Array(dim);
// Xavier initialization
const scale = Math.sqrt(2 / dim);
for (let i = 0; i < dim; i++) {
this.policyWeights[i] = (Math.random() - 0.5) * scale;
this.valueWeights[i] = (Math.random() - 0.5) * scale;
}
}
/**
* Add experience from trajectory
*/
addExperience(trajectory: Trajectory): void {
if (trajectory.steps.length === 0) return;
// Compute values for each step
const values = trajectory.steps.map(step =>
this.computeValue(step.stateAfter)
);
// Compute advantages using GAE
const advantages = this.computeGAE(
trajectory.steps.map(s => s.reward),
values
);
// Compute returns
const returns = this.computeReturns(
trajectory.steps.map(s => s.reward)
);
// Add to buffer
for (let i = 0; i < trajectory.steps.length; i++) {
const step = trajectory.steps[i];
this.buffer.push({
state: step.stateAfter,
action: this.hashAction(step.action),
reward: step.reward,
value: values[i],
logProb: this.computeLogProb(step.stateAfter, step.action),
advantage: advantages[i],
return_: returns[i],
});
}
}
/**
* Perform PPO update
* Target: <10ms
*/
update(): { policyLoss: number; valueLoss: number; entropy: number } {
const startTime = performance.now();
if (this.buffer.length < this.config.miniBatchSize) {
return { policyLoss: 0, valueLoss: 0, entropy: 0 };
}
// Normalize advantages
const advantages = this.buffer.map(e => e.advantage);
const advMean = advantages.reduce((a, b) => a + b, 0) / advantages.length;
const advStd = Math.sqrt(
advantages.reduce((a, b) => a + (b - advMean) ** 2, 0) / advantages.length
) + 1e-8;
for (const exp of this.buffer) {
exp.advantage = (exp.advantage - advMean) / advStd;
}
let totalPolicyLoss = 0;
let totalValueLoss = 0;
let totalEntropy = 0;
let totalClipFrac = 0;
let totalKL = 0;
let numUpdates = 0;
// Multiple epochs
for (let epoch = 0; epoch < this.config.epochs; epoch++) {
// Shuffle buffer
this.shuffleBuffer();
// Process mini-batches
for (let i = 0; i < this.buffer.length; i += this.config.miniBatchSize) {
const batch = this.buffer.slice(i, i + this.config.miniBatchSize);
if (batch.length < this.config.miniBatchSize / 2) continue;
const result = this.updateMiniBatch(batch);
totalPolicyLoss += result.policyLoss;
totalValueLoss += result.valueLoss;
totalEntropy += result.entropy;
totalClipFrac += result.clipFrac;
totalKL += result.kl;
numUpdates++;
// Early stopping if KL too high
if (result.kl > this.config.targetKL * 1.5) {
break;
}
}
}
// Clear buffer
this.buffer = [];
this.updateCount++;
const elapsed = performance.now() - startTime;
if (elapsed > 10) {
console.warn(`PPO update exceeded target: ${elapsed.toFixed(2)}ms > 10ms`);
}
return {
policyLoss: numUpdates > 0 ? totalPolicyLoss / numUpdates : 0,
valueLoss: numUpdates > 0 ? totalValueLoss / numUpdates : 0,
entropy: numUpdates > 0 ? totalEntropy / numUpdates : 0,
};
}
/**
* Get action from policy
*/
getAction(state: Float32Array): { action: number; logProb: number; value: number } {
const logits = this.computeLogits(state);
const probs = this.softmax(logits);
const action = this.sampleAction(probs);
return {
action,
logProb: Math.log(probs[action] + 1e-8),
value: this.computeValue(state),
};
}
/**
* Get statistics
*/
getStats(): Record<string, number> {
return {
updateCount: this.updateCount,
bufferSize: this.buffer.length,
avgLoss: this.updateCount > 0 ? this.totalLoss / this.updateCount : 0,
approxKL: this.approxKL,
clipFraction: this.clipFraction,
};
}
// ==========================================================================
// Private Methods
// ==========================================================================
private computeValue(state: Float32Array): number {
let value = 0;
for (let i = 0; i < Math.min(state.length, this.valueWeights.length); i++) {
value += state[i] * this.valueWeights[i];
}
return value;
}
private computeLogits(state: Float32Array): Float32Array {
// Simplified: 4 discrete actions
const numActions = 4;
const logits = new Float32Array(numActions);
for (let a = 0; a < numActions; a++) {
for (let i = 0; i < Math.min(state.length, this.policyWeights.length); i++) {
logits[a] += state[i] * this.policyWeights[i] * (1 + a * 0.1);
}
}
return logits;
}
private computeLogProb(state: Float32Array, action: string): number {
const logits = this.computeLogits(state);
const probs = this.softmax(logits);
const actionIdx = this.hashAction(action);
return Math.log(probs[actionIdx] + 1e-8);
}
private hashAction(action: string): number {
// Simple hash to action index (0-3)
let hash = 0;
for (let i = 0; i < action.length; i++) {
hash = (hash * 31 + action.charCodeAt(i)) % 4;
}
return hash;
}
private softmax(logits: Float32Array): Float32Array {
const max = Math.max(...logits);
const exps = new Float32Array(logits.length);
let sum = 0;
for (let i = 0; i < logits.length; i++) {
exps[i] = Math.exp(logits[i] - max);
sum += exps[i];
}
for (let i = 0; i < exps.length; i++) {
exps[i] /= sum;
}
return exps;
}
private sampleAction(probs: Float32Array): number {
const r = Math.random();
let cumSum = 0;
for (let i = 0; i < probs.length; i++) {
cumSum += probs[i];
if (r < cumSum) return i;
}
return probs.length - 1;
}
private computeGAE(rewards: number[], values: number[]): number[] {
const advantages = new Array(rewards.length).fill(0);
let lastGae = 0;
for (let t = rewards.length - 1; t >= 0; t--) {
const nextValue = t < rewards.length - 1 ? values[t + 1] : 0;
const delta = rewards[t] + this.config.gamma * nextValue - values[t];
lastGae = delta + this.config.gamma * this.config.gaeLambda * lastGae;
advantages[t] = lastGae;
}
return advantages;
}
private computeReturns(rewards: number[]): number[] {
const returns = new Array(rewards.length).fill(0);
let cumReturn = 0;
for (let t = rewards.length - 1; t >= 0; t--) {
cumReturn = rewards[t] + this.config.gamma * cumReturn;
returns[t] = cumReturn;
}
return returns;
}
private shuffleBuffer(): void {
for (let i = this.buffer.length - 1; i > 0; i--) {
const j = Math.floor(Math.random() * (i + 1));
[this.buffer[i], this.buffer[j]] = [this.buffer[j], this.buffer[i]];
}
}
private updateMiniBatch(batch: PPOExperience[]): {
policyLoss: number;
valueLoss: number;
entropy: number;
clipFrac: number;
kl: number;
} {
let policyLoss = 0;
let valueLoss = 0;
let entropy = 0;
let clipFrac = 0;
let kl = 0;
const policyGrad = new Float32Array(this.policyWeights.length);
const valueGrad = new Float32Array(this.valueWeights.length);
for (const exp of batch) {
// Current policy
const logits = this.computeLogits(exp.state);
const probs = this.softmax(logits);
const newLogProb = Math.log(probs[exp.action] + 1e-8);
const currentValue = this.computeValue(exp.state);
// Ratio for PPO
const ratio = Math.exp(newLogProb - exp.logProb);
// Clipped surrogate objective
const surr1 = ratio * exp.advantage;
const surr2 = Math.max(
Math.min(ratio, 1 + this.config.clipRange),
1 - this.config.clipRange
) * exp.advantage;
const policyLossI = -Math.min(surr1, surr2);
policyLoss += policyLossI;
// Track clipping
if (Math.abs(ratio - 1) > this.config.clipRange) {
clipFrac++;
}
// KL divergence approximation
kl += (exp.logProb - newLogProb);
// Value loss
let valueLossI: number;
if (this.config.clipRangeVf !== null) {
const valuePred = currentValue;
const valueClipped = exp.value + Math.max(
Math.min(valuePred - exp.value, this.config.clipRangeVf),
-this.config.clipRangeVf
);
const vf1 = (valuePred - exp.return_) ** 2;
const vf2 = (valueClipped - exp.return_) ** 2;
valueLossI = Math.max(vf1, vf2);
} else {
valueLossI = (currentValue - exp.return_) ** 2;
}
valueLoss += valueLossI;
// Entropy
let entropyI = 0;
for (const p of probs) {
if (p > 0) entropyI -= p * Math.log(p);
}
entropy += entropyI;
// Compute gradients (simplified)
for (let i = 0; i < Math.min(exp.state.length, policyGrad.length); i++) {
policyGrad[i] += exp.state[i] * policyLossI * 0.01;
valueGrad[i] += exp.state[i] * valueLossI * 0.01;
}
}
// Apply gradients with momentum
const lr = this.config.learningRate;
const beta = 0.9;
for (let i = 0; i < this.policyWeights.length; i++) {
this.policyMomentum[i] = beta * this.policyMomentum[i] + (1 - beta) * policyGrad[i];
this.policyWeights[i] -= lr * this.policyMomentum[i];
this.valueMomentum[i] = beta * this.valueMomentum[i] + (1 - beta) * valueGrad[i];
this.valueWeights[i] -= lr * this.valueMomentum[i];
}
return {
policyLoss: policyLoss / batch.length,
valueLoss: valueLoss / batch.length,
entropy: entropy / batch.length,
clipFrac: clipFrac / batch.length,
kl: kl / batch.length,
};
}
}
/**
* Factory function
*/
export function createPPO(config?: Partial<PPOConfig>): PPOAlgorithm {
return new PPOAlgorithm(config);
}