🎮 Multi-Agent Reinforcement Learning

// MARL学习范式 interface MARLParadigm { // 1. 独立学习 - 每个Agent独立学习 independentLearning(agents: Agent[]): Policy[]; // 2. 集中训练 - 集中训练，分布执行 centralizedTraining(agents: Agent[], env: Environment): Policy[]; // 3. 通信学习 - Agent之间可以通信 communicationLearning(agents: Agent[]): Policy[]; // 4. 分层学习 - 分层决策 hierarchicalLearning(agents: Agent[]): Policy[]; } // 典型算法 enum MARLAlgorithm { "IQL", // Independent Q-Learning "QMIX", // Value Decomposition "MADDPG", // Multi-Agent DDPG "MAPPO", // Multi-Agent PPO "CommNet" // Communication Network }

// OpenClaw配置 - MARL训练 { "name": "agent-strategy-training", "type": "marl", "agents": [ { "id": "content-agent", "role": "content-writer", "algorithm": "MAPPO" }, { "id": "review-agent", "role": "content-reviewer", "algorithm": "MAPPO" } ], "environment": { "type": "content-production", "metrics": ["quality", "speed", "engagement"] }, "training": { "episodes": 10000, "batchSize": 64, "learningRate": 0.001 } }

// 竞争性内容生成 class CompetitiveContentGeneration { async train() { // 1. 创建竞争环境 const env = new ContentArena({ agents: ['writer-a', 'writer-b'], metrics: ['quality', 'originality', 'engagement'] }); // 2. 训练循环 for (let episode = 0; episode < 10000; episode++) { // 重置环境 const topic = env.reset(); // Agent竞争生成 const results = await Promise.all([ env.step('writer-a', topic), env.step('writer-b', topic) ]); // 评估和奖励 const rewards = env.evaluate(results); // 学习 await env.learn(results, rewards); } } }

// marl-training-system.ts import { OpenClaw, Agent, Environment, Policy } from 'openclaw'; class MARLTrainingSystem { private agents: Agent[] = []; private environment: Environment; private policies: Map<string, Policy> = new Map(); constructor() { this.environment = new Environment({ type: 'multi-agent', sharedState: true }); } async train(config: TrainingConfig) { // 1. 初始化Agent for (const agentConfig of config.agents) { const agent = new Agent(agentConfig); const policy = new Policy({ algorithm: agentConfig.algorithm, inputDim: this.environment.observationDim, outputDim: this.environment.actionDim }); this.agents.push(agent); this.policies.set(agent.id, policy); } // 2. 训练循环 for (let episode = 0; episode < config.episodes; episode++) { // 重置环境 let state = this.environment.reset(); let done = false; // episode循环 while (!done) { // 每个Agent决策 const actions: Map<string, Action> = new Map(); for (const agent of this.agents) { const policy = this.policies.get(agent.id)!; const action = await policy.selectAction(state); actions.set(agent.id, action); } // 执行动作 const { nextState, rewards, isDone } = await this.environment.step(actions); // 学习 for (const agent of this.agents) { const policy = this.policies.get(agent.id)!; await policy.learn({ state, action: actions.get(agent.id)!, reward: rewards.get(agent.id)!, nextState, done: isDone }); } state = nextState; done = isDone; } // 评估和日志 if (episode % 100 === 0) { const metrics = this.evaluate(); console.log(`Episode ${episode}: ${JSON.stringify(metrics)}`); } } } private evaluate() { // 评估Agent性能 return { averageReward: this.calculateAverageReward(), collaborationScore: this.calculateCollaboration(), competitionScore: this.calculateCompetition() }; } } // 使用示例 const system = new MARLTrainingSystem(); await system.train({ agents: [ { id: 'writer', algorithm: 'MAPPO' }, { id: 'reviewer', algorithm: 'MAPPO' } ], episodes: 10000 });