Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform-Reference-Cited by-同舟云学术

Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform

Published:2021 Issue: Volume: Page:503-562
ISSN:1860-949X
Container-title:Deep Learning for Unmanned Systems
language:
Short-container-title:

Author:

Khan Adil,Naeem Muhammad,Khattak Asad Masood,Asghar Muhammad Zubair,Malik Abdul Haseeb

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-77939-9_15

Reference61 articles.

1. Khan A, Yang K, Fu Y, Lou F, Jifara W, Jiang F, Shaohui L (2017) A competitive combat strategy and tactics in RTS games AI and starcraft. In: Pacific Rim conference on multimedia 2017. Springer International Publishing, Cham

2. Stanescu M, Čertický M (2016) Predicting opponent’s production in real-time strategy games with answer set programming. IEEE Trans Comput Intell AI Games 8(1):89–94

3. Adil K, Jiang F, Liu S, Jifara W, Tian Z, Fu Y (2017) State-of-the-art and open challenges in RTS Game-AI and starcraft. (IJACSA). Int J Adv Comput Sci Appl 8(12):9

4. Asada M, Noda S, Tawaratsumida S, Hosoda K (1996) Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Mach Learn 23(2):279–303

5. Daylamani-Zad D, Graham LB, Paraskevopoulos IT. Chain of command in autonomous cooperative agents for battles in real-time strategy games. J Comput Educ:1–32

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on Naval Air Defense Intelligent Operations on Deep Reinforcement Learning;2022 34th Chinese Control and Decision Conference (CCDC);2022-08-15