“第三十二章 AI游戏”版本间的差异
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* [[教学参考-32|教学参考]] | * [[教学参考-32|教学参考]] | ||
* [http://aigraph.cslt.org/courses/32/course-32.pptx 课件] | * [http://aigraph.cslt.org/courses/32/course-32.pptx 课件] | ||
| − | * 小清爱提问：机器如何学会打游戏？ | + | * 小清爱提问：机器如何学会打游戏？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247485815&idx=1&sn=e7f80182a71b1820fd52266faef4f45e&chksm=c30803b5f47f8aa3ae32e138b67447318fd64c1b42837447073e58de7241cd348da5aa136a17&scene=178#rd] |
==扩展阅读== | ==扩展阅读== | ||
| − | *AI 100问：机器如何学会打游戏？ [[http://aigraph.cslt.org/ai100/AI-100-82-机器如何学会打游戏.pdf] | + | |
| + | * AI 100问：机器如何学会打游戏？ [[http://aigraph.cslt.org/ai100/AI-100-82-机器如何学会打游戏.pdf] | ||
* OpenAI 捉迷藏游戏[https://openai.com/blog/emergent-tool-use/] | * OpenAI 捉迷藏游戏[https://openai.com/blog/emergent-tool-use/] | ||
* DeepMind AlphaStar 博客 [https://www.deepmind.com/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii] | * DeepMind AlphaStar 博客 [https://www.deepmind.com/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii] | ||
| + | * AlphaStar真的智能了吗？ [https://www.sohu.com/a/294455221_610473] | ||
| + | * DeepMind最强星际争霸AI—— AlphaStar的复现 [https://zhuanlan.zhihu.com/p/56539931] | ||
| − | |||
| + | |||
| + | ==视频展示== | ||
| + | * Deep Mind Atari game playing [http://aigraph.cslt.org/courses/32/Atari.mp4] | ||
* OpenAI Hide and Seek [http://aigraph.cslt.org/courses/32/Multi-Agent.mp4] | * OpenAI Hide and Seek [http://aigraph.cslt.org/courses/32/Multi-Agent.mp4] | ||
| + | * AlphaStar [http://aigraph.cslt.org/courses/32/AlphaStar.mp4] | ||
| + | * Bilibili: AlphaStar 开发纪录片 [https://www.bilibili.com/video/BV1Gb411y7BN?spm_id_from=333.337.search-card.all.click] | ||
| + | * Bilibili: AlphaStar的对战场面 [https://www.bilibili.com/video/BV1Ft411t7Ex?spm_id_from=333.337.search-card.all.click] | ||
| + | |||
==演示链接== | ==演示链接== | ||
| − | + | * 斗地主在线演示 [https://www.douzero.org/] | |
==开发者资源== | ==开发者资源== | ||
| − | + | * 斗地主 [*][https://github.com/kwai/DouZero/blob/main/README.zh-CN.md] | |
==高级读者== | ==高级读者== | ||
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* Baker B, Kanitscheider I, Markov T, et al. Emergent tool use from multi-agent autocurricula[J]. arXiv preprint arXiv:1909.07528, 2019. [https://arxiv.org/pdf/1909.07528] | * Baker B, Kanitscheider I, Markov T, et al. Emergent tool use from multi-agent autocurricula[J]. arXiv preprint arXiv:1909.07528, 2019. [https://arxiv.org/pdf/1909.07528] | ||
* Arulkumaran K, Cully A, Togelius J. Alphastar: An evolutionary computation perspective[C]//Proceedings of the genetic and evolutionary computation conference companion. 2019: 314-315. [https://arxiv.org/pdf/1902.01724] | * Arulkumaran K, Cully A, Togelius J. Alphastar: An evolutionary computation perspective[C]//Proceedings of the genetic and evolutionary computation conference companion. 2019: 314-315. [https://arxiv.org/pdf/1902.01724] | ||
| + | * Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi, Deep Learning for Video Game Playing [https://arxiv.org/abs/1708.07902][https://github.com/hijkzzz/deep-reinforcement-learning-notes] | ||
2023年8月13日 (日) 02:22的最后版本
教学资料
扩展阅读
- AI 100问：机器如何学会打游戏？ [[2]
- OpenAI 捉迷藏游戏[3]
- DeepMind AlphaStar 博客 [4]
- AlphaStar真的智能了吗？ [5]
- DeepMind最强星际争霸AI—— AlphaStar的复现 [6]
视频展示
- Deep Mind Atari game playing [7]
- OpenAI Hide and Seek [8]
- AlphaStar [9]
- Bilibili: AlphaStar 开发纪录片 [10]
- Bilibili: AlphaStar的对战场面 [11]
演示链接
- 斗地主在线演示 [12]
开发者资源
- 斗地主 [*][13]
高级读者
- Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. nature, 2015, 518(7540): 529-533. [14]
- Baker B, Kanitscheider I, Markov T, et al. Emergent tool use from multi-agent autocurricula[J]. arXiv preprint arXiv:1909.07528, 2019. [15]
- Arulkumaran K, Cully A, Togelius J. Alphastar: An evolutionary computation perspective[C]//Proceedings of the genetic and evolutionary computation conference companion. 2019: 314-315. [16]
- Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi, Deep Learning for Video Game Playing [17][18]
