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English(EN) I Trained an AI Agent to Play a Game for 76 Hours (And It Learned From Its Mistakes)

AI代理从零开始学习游戏,但复杂的失败学习机制适得其反

一个AI代理使用Double DQN算法从零开始训练,以玩一款横版跑酷游戏。在笔记本CPU上经过76小时和2.26亿步的学习后,该代理在更难的关卡上表现优于人类设计的玩家。有趣的是,两个旨在改进从失败中学习的自定义机制实际上阻碍了性能,而最简单的机制被证明是最关键的。该代理还表现出随着时间推移失去学习能力的倾向,这一现象已被近期研究预测,尽管修复它并不总是能改善游戏玩法。 AI

影响 展示了基础强化学习技术的有效性,并突出了过度复杂化学习机制的潜在陷阱。

排序理由 该项目描述了强化学习代理的自定义实现及其训练过程,属于研究范畴。[lever_c_research降级:ic=1 ai=1.0]

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AI代理从零开始学习游戏,但复杂的失败学习机制适得其反

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该项目描述了强化学习代理的自定义实现及其训练过程,属于研究范畴。[lever_c_research降级:ic=1 ai=1.0]
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报道来源 [1]

  1. Towards AI TIER_1 English(EN) · Abdul M. ·

    我训练了一个AI代理玩游戏76小时(它学会了从错误中学习)

    <h4><em>A from-scratch DQN trained for 76 hours on a laptop CPU. In 15 ablation runs, the plainest mechanism mattered most, and two of the clever ones hurt.</em></h4><figure><img alt="" src="https://cdn-images-1.medium.com/max/1024/1*RiMeactf_nZo8jCkdKQJHw.png" /></figure><p>I wa…