“第三十二章 AI游戏”版本间的差异
来自cslt Wiki
| 第34行： | 第34行： | ||
* 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] | ||
2022年8月13日 (六) 09:21的版本
教学资料
扩展阅读
视频展示
- Deep Mind Atari game playing [5]
- OpenAI Hide and Seek [6]
- AlphaStar [7]
- Bilibili: AlphaStar 开发纪录片 [8]
- Bilibili: AlphaStar的对战场面 [9]
演示链接
- 斗地主在线演示 [10]
开发者资源
- 斗地主 [11]
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. [12]
- Baker B, Kanitscheider I, Markov T, et al. Emergent tool use from multi-agent autocurricula[J]. arXiv preprint arXiv:1909.07528, 2019. [13]
- Arulkumaran K, Cully A, Togelius J. Alphastar: An evolutionary computation perspective[C]//Proceedings of the genetic and evolutionary computation conference companion. 2019: 314-315. [14]
- Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi, Deep Learning for Video Game Playing [15][16]
