通过深度强化学习来控制人类水平的控制
Volodymyr Mnih1, Koray Kavukcuoglu1, David Silver1
1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|February 27, 2015
概括
这项研究介绍了深度Q网络,这是一种人工智能,可以通过端到端的强化学习从高维感官输入中学习. 该代理在Atari游戏中实现了人类水平的性能,从原始像素数据中展示了有效的概括.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 强化学习 (RL) 优化了基于心理和神经科学原则的代理控制.
- 现实世界的RL需要代理人从高维的感官输入中提取高效的表示,以便进行概括.
- 现有的RL代理仅限于手工制作的特征或低维的,完全观察到的状态.
研究的目的:
- 开发一种能够从高维感官输入中进行端到端强化学习的新型人工智能.
- 在复杂的现实场景中克服以前的RL代理商的局限性.
- 弥合原始感官数据和人工智能中有效决策之间的差距.
主要方法:
- 利用深度神经网络训练的进展来创建一个深度Q网络代理.
- 采用端到端强化学习,仅处理原始像素和游戏分数作为输入.
- 在 49 款经典的 Atari 2600 游戏中测试了该代理.
主要成果:
- 深度Q网络代理在Atari 2600游戏中超越了以前的所有算法.
- 在测试的游戏中实现了与专业的人类游戏测试人员可比的性能.
- 从高维度视觉输入直接展示了成功的学习和概括.
结论:
- 深度Q网络代表了人工智能的重大进步,可以从原始感官数据中学习.
- 这种方法弥合了高维输入和行动之间的沟,创造了多功能代理.
- 代理人在各种具有挑战性的任务中取得的成功凸显了深度强化学习的潜力.
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