You see Reinhardt in nearly every competitive match and even quickplay, anymore. But even then it's such a crawl waiting for the meta to balance out. Is this what the devs had in mind? You can bet not, and that's why they consistently nerf high-tier characters and buff low-tier ones. That's almost half the roster that you will literally never see in any competitive or high-level play. It's almost impossible.įor example, Overwatch's current meta (see below) has 10 characters out of a roster of 22 that see 0 competitive play, or nearly 0. In a way it exposes human frailty when trying to create balanced systems. This has taken away much of the magic of certain games for me, because it exposes glaring flaws in game design and reinforces the idea that a game will never truly be balanced. The loss of the idea that a game should be perfectly or nearly perfectly balanced. In a way, the way competitive games naturally come up with the meta, is the loss of innocence. This concept is something that, as a child, would never have occurred to me. You exploit certain characters' strengths, and exploit other characters' weakness, thereby making them often useless. "The Meta" is basically another version of min-maxing. r/CoOpGaming - A community for co-op gamingĬan we talk about "The Meta" and how it affects competitive games?
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r/pcgaming - PC gaming-specific subreddit for general PC gaming news, discussion and gaming tech support r/nintendo - Nintendo-specific subreddit for general Nintendo news and discussion r/shouldibuythisgame - Find out what's worth getting. r/gamingsuggestions - Go here to help you find your next game to play r/gaming4gamers - Discussion, bar the Hivemind Top-level comments must be at least 100 characters in length.Accounts must be at least one month old.External Links must follow these guidelines No topics that belong in other subreddits This subreddit shouldn't be used for advice of any kind. Use sufficient detail and examples from multiple sources.Clearly define the purpose of your post.Engage in good faith with the points the person you're replying to is making.No discrimination or “isms” of any kind (racism, sexism, etc).Discuss GamingĪll discussion must be about gaming 2. Thomas, L.C.: Games, Theory and Applications.DARK MODE NORMAL MODE Rules 1. Technical report, Stanford University (2003) Shoham, Y., Powers, R., Grenager, T.: Multi-agent reinforcement learning: a critical survey. In: Proceedings of the Eighteenth International Conference on Machine Learning, June 2001, pp. Littman, M.L.: Friend-or-foe q-learning in general-sum games. In: Eleventh International Conference on Machine Learning, New Brunswick, pp. Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. Hu, J., Wellman, M.P.: Nash q-learning for general-sum stochastic games. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. Hu, J., Wellman, M.P.: Multiagent reinforcement learning: theoretical framework and an algorithm.
In: Proceedings of the Twentieth International Conference on, Washington DC, pp. Greenwald, A., Hall, K., Serrano, R.: Correlated-q learning. In: Proceedings of the Fifteenth National Conference on Artificail Intelligence, pp. Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems.
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