基于人群的强化学习的3D多人游戏中的人类水平表现
Max Jaderberg1, Wojciech M Czarnecki1, Iain Dunning2
1DeepMind, London, UK. lejlot@google.com jaderberg@google.com.
概括
在Quake III Arena中,多种强化学习剂达到人类水平的性能. 这些人工智能代理只使用游戏像素和分数来学习复杂的策略,
科学领域:
- 人工智能
- 机器学习
- 多代理系统
背景情况:
- 强化学习 (RL) 在单人游戏和双人游戏中表现出色.
- 现实世界的场景涉及多个独立的代理合作和竞争.
- 现有的RL方法经常与复杂的多代理动态作斗争.
研究的目的:
- 在复杂的动态环境中评估多代理强化学习 (MARL) 的潜力.
- 开发一个能够在多人游戏中达到人类水平的人工智能代理.
- 仅使用原始感官输入和游戏目标来研究学习策略.
主要方法:
- 使用了比赛式的评估框架.
- 使用双层优化过程同时训练了一组独立的RL代理.
- 在随机生成的环境中, 代理从数千个平行匹配中学习.
- 输入仅包括像素和游戏积分.
主要成果:
- 在Quake III竞技场的捕获旗模式中达到人类水平的表现.
- 证明代理人可以学习复杂的合作和竞争行为.
- 代理商独立开发了内部奖励信号和丰富的世界表现.
- 通过高维度感官输入展示了有效的学习.
结论:
- 多代理增强学习对人工智能的发展具有重大潜力.
- 这种方法使人工智能代理能够在动态的多代理环境中掌握复杂的任务.
- 这些研究结果表明,人工智能可以在现实世界中有效运作.
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