Python机器学习项目
本教程中，你为多个游戏构建了机器人，并探索了机器学习中的一个基本概念——偏差-方差 (bias-variance)。接下来很自然会问：你能为像《星际争霸 2》这样更复杂的游戏构建机器人吗？事实证明，这是一个悬而未决的研究问题，并得到了来自谷歌、DeepMind 和暴雪等合作方的开源工具的支持。如果你对这些问题感兴趣，可以查阅 OpenAI 当前的研究开放征集 (open calls for research)。
本教程的核心要点是偏差-方差权衡。机器学习实践者需要考虑模型复杂度的影响。虽然可以利用高度复杂的模型并投入过多的计算资源、样本和时间，但降低模型复杂度可以显著减少所需的资源。
In this tutorial, you built several bots for games and explored a
fundamental concept in machine learning called bias-variance. A natural
next question is: Can you build bots for more complex games, such as
StarCraft 2? As it turns out, this is a pending research question,
supplemented with open-source tools from collaborators across Google,
DeepMind, and Blizzard. If these are problems that interest you, see open
calls for research at OpenAI, for current problems.
The main takeaway from this tutorial is the bias-variance tradeoff. It is
up to the machine learning practitioner to consider the effects of model
complexity. Whereas it is possible to leverage highly complex models and
layer on excessive amounts of compute, samples, and time, reduced
model complexity could significantly reduce the resources required.