![]() Ideally you will inject each base every 30 seconds (when the injects pop and the inject Queen has enough energy for another inject). Once you have set up your camera locations, you will use them to alternate through your bases and use a single injecting Queen which you should make for each base to inject those bases. I would recommend starting with only camera location injects, however as you improve you can start using the inject Queen control group method in the later stages of the game. You will use camera locations for all methods of injecting, however there are two main ways of injecting. Injecting MethodsĬheck out Lambo’s video on inject methods here. Once you have set each camera location, you have taken each base, you can recenter the camera on that base and then remake the camera location so that it is centered. Roughly set camera locations for your bases at the start of the game generally when there is some down time in your build order. If you are comfortable with using more camera locations go for it, however I find the later ones are mostly useful for transferring workers as well as taking bases a little faster, which isn’t a huge deal. However with this being said, I personally use more than 4 usually around 6 and set 7-8. It is recommended to have at least 4 camera location hotkeys that you are comfortable with, the first 4 are by far the most used and most useful in a game as you generally won’t be injecting more than 4 hatcheries regularly and for everything else you can click on the mini map. They are crucial for injecting your hatcheries, responding to attacks and generally allowing you to move around the map faster. 45–59.Camera location hotkeys are an important mechanic in the game as they allow you to more easily accomplish multiple tasks. In: Artificial and Computational Intelligence in Games, pp. Yannakakis, G., Spronck, P., Loiacono, D., Andre, E.: Player modeling. Vinyals, O., et al.: Starcraft II: a new challenge for reinforcement learning. Sweetser, P., Wyeth, P.: Gameflow: a model for evaluating player enjoyment in games. Sun, P., et al.: Tstarbots: defeating the cheating level builtin AI in starcraft II in the full game. Silver, D., et al.: Mastering the game of go without human knowledge. Silver, D., et al.: Mastering chess and shogi by self-play with a general reinforcement learning algorithm. Shao, K., Zhu, Y., Zhao, D.: Starcraft micromanagement with reinforcement learning and curriculum transfer learning. In: 2019 IEEE Conference on Games (CoG), pp. Sarkar, A., Cooper, S.: Inferring and comparing game difficulty curves using player-vs-level match data. In: Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference (2016) Ravari, Y.N., Bakkes, S., Spronck, P.: Starcraft winner prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. Pang, Z.J., Liu, R.Z., Meng, Z.Y., Zhang, Y., Yu, Y., Lu, T.: On reinforcement learning for full-length game of starcraft. Palencia, D.O.V., Osman, M.: PsyRTS: a web platform for experiments in human decision-making in RTS environments. ![]() Mnih, V., et al.: Human-level control through deep reinforcement learning. Menze, B.H., et al.: A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data. ![]() In: Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference (2018) Lee, D., Tang, H., Zhang, J.O., Xu, H., Darrell, T., Abbeel, P.: Modular architecture for starcraft ii with deep reinforcement learning. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. Kou, Y., Li, Y., Gui, X., Suzuki-Gill, E.: Playing with streakiness in online games: how players perceive and react to winning and losing streaks in league of legends. In: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, pp. Hunicke, R.: The case for dynamic difficulty adjustment in games. Hippe, R., Dornheim, J., Zeitvogel, S., Laubenheimer, A.: Evaluation of machine learning algorithms for smurf detection. AAAI press (2004)Ĭhen, Z., Sun, Y., El-Nasr, M.S., Nguyen, T.H.D.: Player skill decomposition in multiplayer online battle arenas. In: Proceedings of the AAAI-04 Workshop on Challenges in Game AI, pp. In: Ninth Artificial Intelligence and Interactive Digital Entertainment Conference (2013)īuro, M.: Call for AI research in RTS games. Avontuur, T., Spronck, P., Van Zaanen, M.: Player skill modeling in starcraft II.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |