乐观的顺序性多代理强化学习与激励性沟通
Anqi Huang1, Yongli Wang1, Xiaoliang Zhou1
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
概括
乐观的顺序软行为体批评与激励沟通 (OSSMC) 通过探索乐观的Q值和使用激励信息来增强多代理强化学习. 这种新的方法在合作任务中实现了更快的融合和更高的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 多代理系统 多代理系统
背景情况:
- 在多代理强化学习 (MARL) 中,分散执行的集中训练 (CTDE) 是常见的.
- 现有的算法低估了动作值,导致了次优化解决方案.
- 复杂的沟通方法阻碍了对基本信息的关注.
研究的目的:
- 为了引入一种新的方法,乐观的顺序软演员批评与激励性沟通 (OSSMC).
- 在CTDE MARL算法中解决非最佳收和通信复杂性.
主要方法:
- OSSMC使用乐观的Q值来贪地探索个人政策潜力.
- 具有乐观的Q值的连续更新机制确保了单调的共同政策改进.
- 激励性沟通模块促进合作行为.
- 软演员关键 (SAC) 值规范化增强了和探索.
主要成果:
- 与基线算法相比,OSSMC在基准数据集上的表现优越.
- 提出的方法表现出明显更快的收率.
- 经验结果验证了乐观的Q值和动机沟通的有效性.
结论:
- OSSMC有效地克服了现有的CTDE MARL算法的局限性.
- 该方法促进了更快的学习和更好的合作策略.
- 在完全合作的MARL.中,OSSMC代表了一个有前途的进步.
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